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Parkinson's disease (PD) is a condition that degenerates over time and impairs speech and pronunciation because brain cells have died. This research work aims to predict parkinson disease using the voice features extracted from speech signals recorded from PD individuals with dysphonic speech disorders by employing deep learning algorithms. PD is challenging to diagnose early on in the clinical presentation. To address the issue in machine learning methods, this paper proposes a neural network model by processing speech signals to classify PD using the University of California Irvine (UCI) machine learning repository dataset. Initially, a pre-loss reduction module is created by using pre-sampling to make the dataset balanced by reducing the dimensionality and maintaining the size of the space without influencing the learning process for data preparation. The relevant features are derived using a novel multi-agent salp swarm (MASS) algorithm, and a novel Parkinson classification neural network (PCNN) is proposed to classify Parkinson's patients with high accuracy employing these derived features. The result shows that the models that use MASS-PCNN produce higher classification accuracy of 99.1%, precision of 97.8%, recall of 94.7% and F1-score of 0.995 when paralleled to the existing models. As an outcome, the suggested model will perform superior to common convolutional neural networks. [ABSTRACT FROM AUTHOR]

With the development of deep learning techniques, the field of direction of arrival (DOA) estimation has also made significant progress. However, the accuracy of DOA estimation using end-to-end neural networks (NNs) heavily relies on the classification step of the networks, which necessitates the use of large and representative datasets. Additionally, conventional speech presence probability (SPP) estimation methods based on the ideal ratio mask (IRM) may misclassify time-frequency (T-F) bins dominated by non-speech and noise, which hinders the accurate extraction of directional information. To improve the robustness of existing DOA estimation algorithms, this paper proposes a DOA estimation method with T-F bin selection. In terms of output, instead of using IRM-based SPP, our proposed approach focuses on the a posteriori SPP, a deliberate choice aimed at circumventing potential confusion. For input optimization, we construct features that encompass spatial, temporal, and directional information concurrently, and these are coupled with a frequency bin-wise recurrent neural network (RNN) model to attain precise multi-channel SPP estimation. Subsequently, these SPP estimates are utilized to extract local information for DOA estimation. Moreover, the cascaded structure ensures that the model has the ability to complete out-of-label tasks, effectively reducing the dataset requirements by training only a subset of direction information to achieve omnidirectional DOA estimation. Besides, this contributes to the algorithm's ability to eliminate its reliance on the step size, setting it apart from other end-to-end methods. Simulation results validate that the proposed method achieves higher accuracy and lower error compared to both NN-based end-to-end approaches and traditional full-band approaches under various conditions of reverberation and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

While deep neural networks have shown impressive results in automatic speaker recognition and related tasks, it is dissatisfactory how little is understood about what exactly is responsible for these results. Part of the success has been attributed in prior work to their capability to model supra-segmental temporal information (SST), i.e., learn rhythmic-prosodic characteristics of speech in addition to spectral features. In this paper, we (i) present and apply a novel test to quantify to what extent the performance of state-of-the-art neural networks for speaker recognition can be explained by modeling SST; and (ii) present several means to force respective nets to focus more on SST and evaluate their merits. We find that a variety of CNN- and RNN-based neural network architectures for speaker recognition do not model SST to any sufficient degree, even when forced. The results provide a highly relevant basis for impactful future research into better exploitation of the full speech signal and give insights into the inner workings of such networks, enhancing explainability of deep learning for speech technologies. • Literature explains speaker recognition in neural nets by modeling of voice dynmaics. • Diagnostic: We quantify how well deep learning models actually capture dynamics. • Observation: State-of-the-art deep nets do not model speaker prosody but ignore it. • Interpretation as "cheating": Achieving high without putting in due effort. • Outlook: Increasing task difficulty biases models towards prosody, but not enough. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs' internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems. [ABSTRACT FROM AUTHOR]

Acoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes. [ABSTRACT FROM AUTHOR]

Background Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music with less alcohol-related lyrics. Existing methods to quantify alcohol exposure in song lyrics have used manual annotation that is burdensome and time intensive. In this paper, we aim to build a deep learning algorithm (LYDIA) that can automatically detect and identify alcohol exposure and its context in song lyrics. Methods We identified 673 potentially alcohol-related words including brand names, urban slang, and beverage names. We collected all the lyrics from the Billboard's top-100 songs from 1959 to 2020 (N = 6110). We developed an annotation tool to annotate both the alcohol-relation of the word (alcohol, non-alcohol, or unsure) and the context (positive, negative, or neutral) of the word in the song lyrics. Results LYDIA achieved an accuracy of 86.6% in identifying the alcohol-relation of the word, and 72.9% in identifying its context. LYDIA can distinguish with an accuracy of 97.24% between the words that have positive and negative relation to alcohol; and with an accuracy of 98.37% between the positive and negative context. Conclusion LYDIA can automatically identify alcohol exposure and its context in song lyrics, which will allow for the swift analysis of future lyrics and can be used to help raise awareness about the amount of alcohol in music. Highlights Developed a deep learning algorithm (LYDIA) to identify alcohol words in songs. LYDIA achieved an accuracy of 86.6% in identifying alcohol-relation of the words. LYDIA's accuracy in identifying positive, negative, or neutral context was 72.9%. LYDIA can automatically provide evidence of alcohol in millions of songs. This can raise awareness of harms of listening to songs with alcohol words. [ABSTRACT FROM AUTHOR]

Automatic speech recognition (ASR) is a typical pattern recognition technology that converts human speeches into texts. With the aid of advanced deep learning models, the performance of speech recognition is significantly improved. Especially, the emerging Audio–Visual Speech Recognition (AVSR) methods achieve satisfactory performance by combining audio-modal and visual-modal information. However, various complex environments, especially noises, limit the effectiveness of existing methods. In response to the noisy problem, in this paper, we propose a novel cross-modal audio–visual speech recognition model, named CATNet. First, we devise a cross-modal bidirectional fusion model to analyze the close relationship between audio and visual modalities. Second, we propose an audio–visual dual-modal network to preprocess audio and visual information, extract significant features and filter redundant noises. The experimental results demonstrate the effectiveness of CATNet, which achieves excellent WER, CER and converges speeds, outperforms other benchmark models and overcomes the challenge posed by noisy environments. • Proposing a novel cross-modal audio–visual speech recognition network, named CATNet. • Devising a cross-modal bidirectional fusion model. • Devising an audio–visual dual-modal speech recognition network. • CATNet is robust against noises and outperforms other benchmarks. [ABSTRACT FROM AUTHOR]

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper introduces a new method for cleaning impaired speech by combining Pareto-optimized deep learning with Non-negative Matrix Factorization (NMF). The approach effectively reduces noise in impaired speech while preserving the desired speech quality. The method involves calculating the spectrogram of a noisy voice clip, determining a noise threshold, computing a noise-to-signal mask, and smoothing it to avoid abrupt transitions. Using a Pareto-optimized NMF, the modified spectrogram is decomposed into basis functions and weights, allowing for reconstruction of the clean speech spectrogram. The final result is a noise-reduced waveform achieved by inverting the clean speech spectrogram. Experimental results validate the method's effectiveness in cleaning alaryngeal speech signals, indicating its potential for real-world applications. The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications. [ABSTRACT FROM AUTHOR]

In indoor environments, reverberation often distorts clean speech. Although deep learning-based speech dereverberation approaches have shown much better performance than traditional ones, the inferior speech quality of the dereverberated speech caused by magnitude distortion and limited phase recovery is still a serious problem for practical applications. This paper improves the performance of deep learning-based speech dereverberation from the perspectives of both network design and mapping target optimization. Specifically, on the one hand, a bifurcated-and-fusion network and its guidance loss functions were designed to help reduce the magnitude distortion while enhancing the phase recovery. On the other hand, the time boundary between the early and late reflections in the mapped speech was investigated, so as to make a balance between the reverberation tailing effect and the difficulty of magnitude/phase recovery. Mathematical derivations were provided to show the rationality of the specially designed loss functions. Geometric illustrations were given to explain the importance of preserving early reflections in reducing the difficulty of phase recovery. Ablation study results confirmed the validity of the proposed network topology and the importance of preserving 20 ms early reflections in the mapped speech. Objective and subjective test results showed that the proposed system outperformed other baselines in the speech dereverberation task. [ABSTRACT FROM AUTHOR]

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one's cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level. [ABSTRACT FROM AUTHOR]

Speech enhancement is an important method for improving speech quality and intelligibility in noisy environments. An effective speech enhancement model depends on precise modelling of the long-range dependencies of noisy speech. Several recent studies have examined ways to enhance speech by capturing the long-term contextual information. For speech enhancement, the time–frequency (T–F) distribution of speech spectral components is also important, but is usually ignored in these studies. The multi-stage learning method is an effective way to integrate various deep learning modules at the same time. The benefit of multi-stage training is that the optimization target can be iteratively updated stage by stage. In this paper, speech enhancement is investigated by multi-stage learning using a multi-stage structure in which time–frequency attention (TFA) blocks are followed by stacks of squeezed temporal convolutional networks (S-TCN) with exponentially increasing dilation rates. To reinject original information into later stages, a feature fusion block (FB) is inserted at the input of later stages to reduce the possibility of speech information being lost in the early stages. The S-TCN blocks are responsible for temporal sequence modelling tasks. The time–frequency attention (TFA) is a simple but effective network module that explicitly exploits position information to generate a 2D attention map to characterize the salient T–F distribution of speech by using two branches, time-frame attention and frequency attention in parallel. Extensive experiments have demonstrated that the proposed model consistently improves the performance over existing baselines across two widely used objective metrics such as PESQ and STOI. A significant improvement in system robustness to noise is also shown by our evaluation results using the TFA module. [ABSTRACT FROM AUTHOR]

End-to-end deep learning models have shown promising results for the automatic screening of Parkinson's disease by voice and speech. However, these models often suffer degradation in their performance when applied to scenarios involving multiple corpora. In addition, they also show corpus-dependent clusterings. These facts indicate a lack of generalisation or the presence of certain shortcuts in the decision, and also suggest the need for developing new corpus-independent models. In this respect, this work explores the use of domain adversarial training as a viable strategy to develop models that retain their discriminative capacity to detect Parkinson's disease across diverse datasets. The paper presents three deep learning architectures and their domain adversarial counterparts. The models were evaluated with sustained vowels and diadochokinetic recordings extracted from four corpora with different demographics, dialects or languages, and recording conditions. The results showed that the space distribution of the embedding features extracted by the domain adversarial networks exhibits a higher intra-class cohesion. This behaviour is supported by a decrease in the variability and inter-domain divergence computed within each class. The findings suggest that domain adversarial networks are able to learn the common characteristics present in Parkinsonian voice and speech, which are supposed to be corpus, and consequently, language independent. Overall, this effort provides evidence that domain adaptation techniques refine the existing end-to-end deep learning approaches for Parkinson's disease detection from voice and speech, achieving more generalizable models. [ABSTRACT FROM AUTHOR]

Multimodal emotion recognition (MER) refers to the identification and understanding of human emotional states by combining different signals, including—but not limited to—text, speech, and face cues. MER plays a crucial role in the human–computer interaction (HCI) domain. With the recent progression of deep learning technologies and the increasing availability of multimodal datasets, the MER domain has witnessed considerable development, resulting in numerous significant research breakthroughs. However, a conspicuous absence of thorough and focused reviews on these deep learning-based MER achievements is observed. This survey aims to bridge this gap by providing a comprehensive overview of the recent advancements in MER based on deep learning. For an orderly exposition, this paper first outlines a meticulous analysis of the current multimodal datasets, emphasizing their advantages and constraints. Subsequently, we thoroughly scrutinize diverse methods for multimodal emotional feature extraction, highlighting the merits and demerits of each method. Moreover, we perform an exhaustive analysis of various MER algorithms, with particular focus on the model-agnostic fusion methods (including early fusion, late fusion, and hybrid fusion) and fusion based on intermediate layers of deep models (encompassing simple concatenation fusion, utterance-level interaction fusion, and fine-grained interaction fusion). We assess the strengths and weaknesses of these fusion strategies, providing guidance to researchers to help them select the most suitable techniques for their studies. In summary, this survey aims to provide a thorough and insightful review of the field of deep learning-based MER. It is intended as a valuable guide to aid researchers in furthering the evolution of this dynamic and impactful field. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs. [ABSTRACT FROM AUTHOR]

The recognition of human activities using vision-based techniques has become a crucial research field in video analytics. Over the last decade, there have been numerous advancements in deep learning algorithms aimed at accurately detecting complex human actions in video streams. While these algorithms have demonstrated impressive performance in activity recognition, they often exhibit a bias towards either model performance or computational efficiency. This biased trade-off between robustness and efficiency poses challenges when addressing complex human activity recognition problems. To address this issue, this paper presents a computationally efficient yet robust approach, exploiting saliency-aware spatial and temporal features for human action recognition in videos. To achieve effective representation of human actions, we propose an efficient approach called the dual-attentional Residual 3D Convolutional Neural Network (DA-R3DCNN). Our proposed method utilizes a unified channel-spatial attention mechanism, allowing it to efficiently extract significant human-centric features from video frames. By combining dual channel-spatial attention layers with residual 3D convolution layers, the network becomes more discerning in capturing spatial receptive fields containing objects within the feature maps. To assess the effectiveness and robustness of our proposed method, we have conducted extensive experiments on four well-established benchmark datasets for human action recognition. The quantitative results obtained validate the efficiency of our method, showcasing significant improvements in accuracy of up to 11% as compared to state-of-the-art human action recognition methods. Additionally, our evaluation of inference time reveals that the proposed method achieves up to a 74× improvement in frames per second (FPS) compared to existing approaches, thus showing the suitability and effectiveness of the proposed DA-R3DCNN for real-time human activity recognition. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning based approach for frontal gait recognition was investigated and presented. In today’s world, gait recognition is an interesting way for person identification. Person identification is required in various fields of human life and many different methods are used. Some of them are fingerprint, methods based on different elements of the human eye such as the retina or iris, face recognition, speech or voice recognition, gait recognition, etc. It is important to note that some of them, such as gait or face recognition, are suitable for identification at a greater distance and without interaction with a device to capture the different features on which they are based. Gait recognition is a method that uses the manner of human gait for identification. In recent years, many approaches to gait recognition have been developed and presented. In this work, a deep learning approach was developed based on a deep learning model and a gait recognition method called Gait Energy Image (GEI). The experiment was performed using the well–known Casia Dataset B and the results were presented. [ABSTRACT FROM AUTHOR]

Finding ways to enable seamless communication between deaf and able-bodied individuals has been a challenging and pressing issue. This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition. In this study, four machine learning models—decision tree (DT), support vector machine (SVM), K-nearest neighbor method (KNN), and random forest (RF)—were employed to recognize 20 different types of dynamic sign language data used by deaf individuals. Additionally, a proposed attention-based mechanism of long and short-term memory neural networks (Attention-BiLSTM) was utilized in the process. Furthermore, this study verifies the impact of the number and position of data glove nodes on the accuracy of recognizing complex dynamic sign language. Finally, the proposed method is compared with existing state-of-the-art algorithms using nine public datasets. The results indicate that both the Attention-BiLSTM and RF algorithms have the highest performance in recognizing the twenty dynamic sign language gestures, with an accuracy of 98.85% and 97.58%, respectively. This provides evidence for the feasibility of our proposed data glove and recognition methods. This study may serve as a valuable reference for the development of wearable sign language recognition devices and promote easier communication between deaf and able-bodied individuals. [ABSTRACT FROM AUTHOR]

Deep learning is a rapidly growing research area having state of art achievement in various applications including but not limited to speech recognition, object recognition, machine translation, and image segmentation. In the current modern industrial manufacturing system, Machine Health Surveillance System (MHSS) is achieving increasing popularity because of the widespread availability of low cost sensors internet connectivity. Deep learning architecture gives useful tools to analyze and process these vast amounts of machinery data. In this paper, we review the latest deep learning techniques and their variant used for MHSS. We used Gearbox Fault Diagnosis dataset in this paper that contains the sets of vibration attributes recorded by SpectraQuest's Gearbox Fault Diagnostics Simulator. In addition, we used the variant of auto encoders for feature extraction to achieve higher accuracy in machine health surveillance. The results showed that the bagging ensemble classifier based on voting techniques achieved 99% accuracy. [ABSTRACT FROM AUTHOR]

People can experience depression, a mood disorder with wide-ranging effects. Depression can happen for many causes in daily life, which has an impact on our health. Stress, a lack of a work-life balance, and other factors make it a serious issue in the current era. It is a key factor in the overall global burden of disease, a leading cause of disability, and a suicide cause. Therapy is now the only effective treatment for depression. However, given the high demand for psychological therapy, it is challenging to find therapists for all potential depression situations. Additionally, there is no reliable biomarker to detect depression in an individual. The most reliable method for identifying depression is sound. Previous research has demonstrated that sad people exhibit fewer prosodic qualities of sound than a normal person. The paper employs deep learning neural networks to teach the computer how to distinguish between a healthy person's sound and a depressed person's sound using speech emotion identification to identify depression. Utilizing a convolutional neural network and the Mel Frequency Cepstral Coefficient, the relevant sound waves may be retrieved. The result is a trained model that can be used to make predictions. This model is then made available as a web service for service requesters to use. [ABSTRACT FROM AUTHOR]

Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence information from a single aspect, resulting in incomplete information acquisition. Firstly, to address the problems of low text recognition accuracy and poor recognition of irregular text, we add label smoothing to ensure the model's generalization ability. Then, we introduce the smoothing loss function from speech recognition into the field of text recognition, and add a language model to increase information acquisition channels, ultimately achieving the goal of improving text recognition accuracy. This method was experimentally verified on six public datasets and compared with other advanced methods. The experimental results show that this method performs well in most benchmark tests, and the improved model outperforms the original model in recognition performance. [ABSTRACT FROM AUTHOR]

Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient training data, pre-trained models are limited in their generalisation ability, leading to poor performance on novel test sets. To mitigate this challenge, transfer learning performed by fine-tuning pr-etrained models on novel domains has been applied. However, the fine-tuned knowledge may overwrite and/or discard important knowledge learnt in pre-trained models. In this paper, we address this issue by proposing a PathNet-based meta-transfer learning method that is able to (i) transfer emotional knowledge learnt from one visual/audio emotion domain to another domain and (ii) transfer emotional knowledge learnt from multiple audio emotion domains to one another to improve overall emotion recognition accuracy. To show the robustness of our proposed method, extensive experiments on facial expression-based emotion recognition and speech emotion recognition are carried out on three bench-marking data sets: SAVEE, EMODB, and eNTERFACE. Experimental results show that our proposed method achieves superior performance compared with existing transfer learning methods. [ABSTRACT FROM AUTHOR]

The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes. [ABSTRACT FROM AUTHOR]

In recent years, speech processing resides a major application in the domain of signal processing. Due to the audibility loss of some speech signals, people with hearing impairment have difficulty in understanding speech, which reintroduces a crucial role in speech recognition. Automatic Speech Recognition (ASR) development is a major challenge in research in the case of noise, domain, vocabulary size, and language and speaker variability. Speech recognition system design needs careful attention to challenges or issues like performance and database evaluation, feature extraction methods, speech representations and speech classes. In this paper, HDF-DNN model has been proposed with the hybridization of discriminant fuzzy function and deep neural network for speech recognition. Initially, the speech signals are pre-processed to eliminate the unwanted noise and the features are extracted using Mel Frequency Cepstral Coefficient (MFCC). A hybrid Deep Neural Network and Discriminant Fuzzy Logic is used for assisting hearing-impaired listeners with enhanced speech intelligibility. Both DNN and DF have some problems with parameters to address this problem, Enhanced Modularity function-based Bat Algorithm (EMBA) is used as a powerful optimization tool. The experimental results show that the proposed automatic speech recognition-based hybrid deep learning model is effectively-identifies speech recognition more than the MFCC-CNN, CSVM and Deep auto encoder techniques. The proposed method improves the overall accuracy of 8.31%, 9.71% and 10.25% better than, MFCC-CNN, CSVM and Deep auto encoder respectively. [ABSTRACT FROM AUTHOR]

This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term "in the wild" is used to describe AVSR for unconstrained natural-language audio streams and video-stream modalities. Audio-visual speech recognition (AVSR) is a speech-recognition task that leverages both an audio input of a human voice and an aligned visual input of lip motions. However, since in-the-wild scenarios can include more noise, AVSR's performance is affected. Here, we propose new improvements for AVSR models by incorporating data-augmentation techniques to generate more data samples for building the classification models. For the data-augmentation techniques, we utilized a combination of conventional approaches (e.g., flips and rotations), as well as newer approaches, such as generative adversarial networks (GANs). To validate the approaches, we used augmented data from well-known datasets (LRS2—Lip Reading Sentences 2 and LRS3) in the training process and testing was performed using the original data. The study and experimental results indicated that the proposed AVSR model and framework, combined with the augmentation approach, enhanced the performance of the AVSR framework in the wild for noisy datasets. Furthermore, in this study, we discuss the domains of automatic speech recognition (ASR) architectures and audio-visual speech recognition (AVSR) architectures and give a concise summary of the AVSR models that have been proposed. [ABSTRACT FROM AUTHOR]

The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97 % and 99.34 ± 0.66 % , respectively. [ABSTRACT FROM AUTHOR]

Speech reflects people's mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models. [ABSTRACT FROM AUTHOR]

Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%. [ABSTRACT FROM AUTHOR]

This research paper proposes sentiment analysis of pets using deep learning technologies in the artificial intelligence of things system. Mask R-CNN is used to detect image objects and generate contour mask maps, pose analysis algorithms are used to obtain object posture information, and at the same time object sound signals are converted into spectrograms as features, using deep learning image recognition technology to obtain object emotion information. By using the fusion of object posture and emotional characteristics as the basis for pet emotion identification and analysis, the detected specific pet behaviour states will be actively notified to the owner for processing. Compared with traditional speech recognition, which uses mel-frequency cepstral coefficients for feature extraction, coupled with a Gaussian mixture model-hidden Markov model for voice recognition, the experimental method of this research paper effectively improves the accuracy by 70%. Prior work on the implementation of smart pet surveillance systems has used the pet's tail and mouth as features, and has combined these with sound features to analyse the pet's emotions. This research paper proposes a new method of sentiment analysis in pets, and our method is compared with previous related work. Experimental results show that our approach increases the accuracy rate by 70%. [ABSTRACT FROM AUTHOR]

Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis. The strategies employed to investigate their theoretical properties mainly rely on Euclidean geometry, but in the last years new approaches based on Riemannian geometry have been developed. Motivated by some open problems, we study a particular sequence of maps between manifolds, with the last manifold of the sequence equipped with a Riemannian metric. We investigate the structures induced through pullbacks on the other manifolds of the sequence and on some related quotients. In particular, we show that the pullbacks of the final Riemannian metric to any manifolds of the sequence is a degenerate Riemannian metric inducing a structure of pseudometric space. We prove that the Kolmogorov quotient of this pseudometric space yields a smooth manifold, which is the base space of a particular vertical bundle. We investigate the theoretical properties of the maps of such sequence, eventually we focus on the case of maps between manifolds implementing neural networks of practical interest and we present some applications of the geometric framework we introduced in the first part of the paper. • We consider Neural Networks as sequences of smooth maps between manifolds. • We investigate the structures induced through pull-backs on the manifolds. • The pull-backs of the final Riemannian metric to any manifolds is a degenerate metric. • The theoretical study leads to construct equivalence classes in the input manifold. [ABSTRACT FROM AUTHOR]

The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated that CNN-based speech recognition algorithms outperform LDA and off-the-shelf platforms. The ResNet-50 architecture of CNN yielded an accuracy of 99.64 ± 0.26% on the healthy dataset. Even though Microsoft Azure was not trained on the same healthy dataset, it still generated comparable results to the LDA and superior results to Google's speech recognition platform. [ABSTRACT FROM AUTHOR]

Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks (CNNs) and various two-dimensional (2D) speech signal representations. To reduce the computational cost and not resign from the 2D representation-based approach, a strategy for threshold-based averaging of the Lombard effect detection results is introduced. The pseudocode of the averaging process is also included. A series of experiments are performed to determine the most effective network structure and the 2D speech signal representation. Investigations are carried out on German and Polish recordings containing Lombard speech. All 2D signal speech representations are tested with and without augmentation. Augmentation means using the alpha channel to store additional data: gender of the speaker, F0 frequency, and first two MFCCs. The experimental results show that Lombard and neutral speech recordings can clearly be discerned, which is done with high detection accuracy. It is also demonstrated that the proposed speech detection process is capable of working in near real-time. These are the key contributions of this work. [ABSTRACT FROM AUTHOR]

With the rapid progress and wide application of cutting-edge technologies such as automatic control technology and wireless communication technology in recent years, smart home, as an effective combination of these cutting-edge technologies and daily life, has received more and more attention and Research has been greatly developed. In order to solve the shortcomings of the existing home control need to download various cumbersome APP, the WeChat applet is introduced into the smart home system, and the intelligent voice technology is planned to be used in the smart home control to realize the voice control, improve the user experience and make the home Life gets smarter. This time, the deep learning technology was used in the development process of the smart home control system, and according to the historical information of the home, combined with the learning ability of the recurrent neural network for time series data, through the mining, analysis and learning of historical data, the construction based on specific The user's unique computing model can seamlessly connect the capabilities of the voice cloud platform with the capabilities of the Internet of Things cloud platform through the Web server, making it possible to quickly access the voice recognition capabilities to smart homes. The entire system has stable connectivity, easy deployment and low cost. The main functions are deployed in the cloud and extended. After 10 rounds of iterative training of the Attention-GRU model in this paper, its prediction accuracy can quickly rise to about 97%, and finally stabilize at about 98.2%, and the lighting prediction accuracy can reach 95% or higher. [ABSTRACT FROM AUTHOR]

When making voice interactions with hands-free speech communication devices, direction-of-arrival estimation is an essential step. To address the detrimental influence of unavoidable background noise and interference speech on direction-of-arrival estimation, this paper introduces a stacked self-attention network system, a supervised deep learning method that enables utterance level estimation without requirement for any pre-processing such as voice activity detection. Specifically, alternately stacked time- and frequency-dependent self-attention blocks are designed to process information in terms of time and frequency, respectively. The former blocks focus on the importance of each time frame of the received audio mixture and perform temporal selection to reduce the influence of non-speech and interference frames, while the latter blocks are utilized to derive inner-correlation among different frequencies. Additionally, the non-causal convolution and self-attention networks are replaced by causal ones, enabling real-time direction-of-arrival estimation with a latency of only 6.25 ms. Experiments with simulated and measured room impulse responses, as well as real recordings, verify the advantages of the proposed method over the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%. [ABSTRACT FROM AUTHOR]

In this paper, a gait classification method based on the deep belief network (DBN) optimized by the sparrow search algorithm (SSA) is proposed. The multiple features obtained based on surface electromyography (sEMG) are fused. These functions are used to train the model. First, the sample features, such as the time domain and frequency domain features of the denoised sEMG are extracted and then the fused features are obtained by feature combination. Second, the SSA is utilized to optimize the architecture of DBN and its weight parameters. Finally, the optimized DBN classifier is trained and used for gait recognition. The classification results are obtained by varying different factors and the recognition rate is compared with the previous classification algorithms. The results show that the recognition rate of SSA-DBN is higher than other classifiers, and the recognition accuracy is improved by about 2% compared with the unoptimized DBN. This indicates that for the application in gait recognition, SSA can optimize the network performance of DBN, thus improving the classification accuracy. [ABSTRACT FROM AUTHOR]

Human emotion recognition with the evaluation of speech signals is an emerging topic in recent decades. Emotion recognition through speech signals is relatively confusing because of the speaking style, voice quality, cultural background of the speaker, environment, etc. Even though numerous signal processing methods and frameworks exists to detect and characterize the speech signal's emotions, they do not attain the full speech emotion recognition (SER) accuracy and success rate. This paper proposes a novel algorithm, namely the deep ganitrus algorithm (DGA), to perceive the various categories of emotions from the input speech signal for better accuracy. DGA combines independent component analysis with fisher criterion for feature extraction and deep belief network with wake sleep for emotion classification. This algorithm is inspired by the elaeocarpus ganitrus (rudraksha seed), which has 1 to 21 lines. The single line bead is rarest to find, analogously finding a single emotion from the speech signal is also complex. The proposed DGA is experimentally verified on the Berlin database. Finally, the evaluation results were compared with the existing framework, and the test result accomplishes better recognition accuracy when compared with all other current algorithms. [ABSTRACT FROM AUTHOR]

In recent years, research on modulated signal recognition using deep learning (DL) has achieved remarkable success, allowing automatic modulation recognition (AMR) to play a crucial role in modern communication systems. The emergence of the attention mechanism has then rapidly led to a wide range of applications in image classification and speech recognition, which proves the effectiveness of the attention mechanism. In this paper, the authors propose a network model feed‐forward Attention mechanism with Residuals networks and Long Short‐Term Memory (RLADNN) based on feed‐forward attention mechanism with Residuals networks (Resnet) and Long Short‐Term Memory (LSTM), which takes the advantage that the Attention mechanism can effectively solve some long‐term memory problems in [−20:18] signal‐to‐noise ratio (SNR) for the recognition of 11 modulated signals with different SNR, and effectively improves the recognition rate. [ABSTRACT FROM AUTHOR]

With the emergence of the information age, computers have entered the homes of ordinary people and have become essential daily appliances for people. The integration of people and computers has become more popular and in-depth. Based on this situation, how to make computers and humans communicate intelligently and make human-computer interaction more convenient and practical is the main issue of scientific research and discussion on computers. Language recognition is widely used in industry, the economy, commerce, tourism, style, and sports. The BP neural network based on deep learning has supercomputing functions and is very suitable for speech recognition, which promotes the promotion and application of speech recognition technology in many fields. Different types of applications and diverse scenarios enable users to experience different personal experiences. This paper examines the use of application software for ideological and political theory courses in colleges and universities and the application of application software for ideological and political theory courses in colleges and universities in the management of ideological and political theory courses. Analyzed the use of apps in the management of ideological and political theory courses in colleges and universities, analyzed the possibilities and challenges that this brought to the management of ideological and political theory courses in colleges and universities based on the app, and put forward the necessary countermeasures. Including the development and design of ideological and political theory applications, combining deep learning and CTC algorithms to build acoustic models, using server-client interaction, designing an ideological and political theory course app based on speech recognition and deep learning, and forming an offline speech recognition system software platform, the app provides a certain reference for improving the skills of teaching managers. [ABSTRACT FROM AUTHOR]

The location attention mechanism has been widely applied in deep neural networks. However, as the mechanism entails heavy computing workload, significant memories consumed for weights storage, and shows poor parallelism in some calculations, it is hard to achieve high efficiency deployment. In this paper, the field-programmable gate array (FPGA) is employed to implement the location attention mechanism in hardware, and a novel fusion approach is proposed to connect the convolutional layer with the fully connected layer, which not only improves the parallelism of both the algorithm and the hardware pipeline, but also reduces the computation cost for such operations as multiplication and addition. Meanwhile, the shared computing architecture is used to reduce the demand for hardware resources. Parallel computing arrays are utilized to time-multiplex a single computing array, which can speed up the pipeline parallel computing of the attention mechanism. Experimental results show that for the location attention mechanism, the FPGA's inference speed is 0.010 ms, which is around a quarter of the speed achieved by running it with GPU, and its power consumption is 1.73 W, which is about 2.89% of the power consumed by running it with CPU. Compared with other FPGA implementation methods of attention mechanism, it has less hardware resource consumption and less inference time. When applied to speech recognition tasks, the trained attention model is symmetrically quantized and deployed on the FPGA. The result shows that the word error rate is only 0.79% higher than that before quantization, which proves the effectiveness and correctness of the hardware circuit. [ABSTRACT FROM AUTHOR]

As artificial intelligence and industrial automation are developing, human-robot collaboration (HRC) with advanced interaction capabilities has become an increasingly significant area of research. In this paper, we design and develop a real-time, multi-model HRC system using speech and gestures. A set of 16 dynamic gestures is designed for communication from a human to an industrial robot. A data set of dynamic gestures is designed and constructed, and it will be shared with the community. A convolutional neural network is developed to recognize the dynamic gestures in real time using the motion history image and deep learning methods. An improved open-source speech recognizer is used for real-time speech recognition of the human worker. An integration strategy is proposed to integrate the gesture and speech recognition results, and a software interface is designed for system visualization. A multi-threading architecture is constructed for simultaneously operating multiple tasks, including gesture and speech data collection and recognition, data integration, robot control, and software interface operation. The various methods and algorithms are integrated to develop the HRC system, with a platform constructed to demonstrate the system performance. The experimental results validate the feasibility and effectiveness of the proposed algorithms and the HRC system. [ABSTRACT FROM AUTHOR]

Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker's skull and have better noise-resistance capabilities than standard air-conduction microphones (ACMs). BCMs have a different frequency response than ACMs because they only capture the low-frequency portion of speech signals. When we replace an ACM with a BCM, we may get satisfactory noise suppression results, but the speech quality and intelligibility may suffer due to the nature of the solid vibration. Mismatched BCM and ACM characteristics can also have an impact on ASR performance, and it is impossible to recreate a new ASR system using voice data from BCMs. The speech intelligibility of a BCM-conducted speech signal is determined by the location of the bone used to acquire the signal and accurately model phonemes of words. Deep learning techniques such as neural network have traditionally been used for speech recognition. However, neural networks have a high computational cost and are unable to model phonemes in signals. In this paper, the intelligibility of BCM signal speech was evaluated for different bone locations, namely the right ramus, larynx, and right mastoid. Listener and deep learning architectures such as CapsuleNet, UNet, and S-Net were used to acquire the BCM signal for Tamil words and evaluate speech intelligibility. As validated by the listener and deep learning architectures, the Larynx bone location improves speech intelligibility. [ABSTRACT FROM AUTHOR]

Audio processing has become an inseparable part of modern applications in domains ranging from health care to speech-controlled devices. In automated audio segmentation, deep learning plays a vital role. In this article, we are discussing audio segmentation based on deep learning. Audio segmentation divides the digital audio signal into a sequence of segments or frames and then classifies these into various classes such as speech recognition, music, or noise. Segmentation plays an important role in audio signal processing. The most important aspect is to secure a large amount of high-quality data when training a deep learning network. In this study, various application areas, citation records, documents published year-wise, and source-wise analysis are computed using Scopus and Web of Science (WoS) databases. The analysis presented in this paper supports and establishes the significance of the deep learning techniques in audio segmentation. [ABSTRACT FROM AUTHOR]

Conventionally, the extraction of hand-crafted acoustic features has been separated from the task of establishing robust machine-learning models in speech processing. The manual approach of feature engineering is both time-consuming and necessitates specialist knowledge, posing significant hindrances. Moreover, the resulting features may not be ideal for the desired application. The speech community has adopted raw waveform modeling to enhance performance. These techniques learn an optimized representation of the input automatically. With deep learning (DL) advancements, raw waveform modeling has become valuable for tasks like classification and prediction. The primary aim of this survey is to offer valuable insights and fills a gap in the existing literature by providing a comprehensive review of the state-of-the-art in speech and speaker recognition using raw waveform modeling for both adult and children's speech. The article covers papers from 2013–2023 and is the first to review both adult and children's databases. The article focuses on the advantages of raw waveform models. It presents essential concepts and techniques while discussing the challenges and limitations of using raw waveforms for speech and speaker recognition in both adult and children's speech. The article also evaluates recent progress in DL architectures such as SincNet, ResNet, and RawNet, and outlines future research directions in the field. [ABSTRACT FROM AUTHOR]

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation. [ABSTRACT FROM AUTHOR]