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Cross-lingual speech recognition leverages data from a variety of source languages to train systems capable of identifying speech in a target language, thus promoting intercultural communication and understanding. To address the issues of how to utilize multilingual data to improve the recognition performance of low resource languages in cross-lingual speech recognition, domain shift or interference between source and target languages, task weights and data distribution between different languages, a cross lingual speech recognition model was studied through feature prompts. To simplify the traditional process of requiring professionals to label phonemes uniformly, a cross-lingual speech data annotation method was studied by identifying the corresponding language in the original data, and experiments were conducted on two public datasets. The results show that the proposed model achieves a substantial reduction in the average error rate 46.44% lower than the Conformer model, a mainstream speech recognition model, and 2.1% lower than the baseline model, thereby attaining higher accuracy in recognition. The research results provide novel perspectives and methodologies for the domain of cross-lingual speech recognition. [ABSTRACT FROM AUTHOR]

The acoustic input network based on the Conformer encoder has the problem of insufficient extraction of FBank speech information and missing channel feature information. This paper proposed an end-to-end method based on RepVGG-SE-Conformer for speech recognition to solve these problems. Firstly, the proposed model used the multi-branch structure of RepVGG to enhance the speech information extraction capability, and using the structural re-parameterization fused the multibranch into a single branch to reduce the computational complexity and speed up the model inference. Then, based on the squeeze-and-excitation network, the channel attention mechanism made up for the missing channel feature information to improve speech recognition accuracy. Finally, the experimental results on the public dataset Aishell-1 show that the proposed method's character error rate is reduced by 10.67% compared with Conformer, and the advancement of the method is verified. In addition, the proposed RepVGG-SE acoustic input network has good generalization ability in the end-to-end scene, which can effectively improve the overall performance of speech recognition models based on Transformer variants. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

local feature information at shallow layers. To solve this problem, this study proposes a method using multiple encoders to improve the ability of speech feature extraction. An additional convolutional encoder branch is added to strengthen the capture of local feature information, make up for the neglect of local feature information in shallow Transformer, and effectively realize the integration of global and local dependencies of andio feature se quences. In other words, a multi-encoder model hased on Transformer is proposed. Experiments on the open source Chinese Mandarin data set Aishell 1 show that without an external language model, the proposed Transformer-based multi-encoder model has a relative reduction of 4.00% in character error rate when compared with the Transformer model. On the internal non public Shanghainese dialect data set, the performance improve ment of the proposed model is more obvious, and the character error rate is reduced by 48.24% from 19.92% to 10. 31%. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the decrease in accuracy of speech recognition software in noisy environments, a robust enhancement method based on domain knowledge is proposed to ensure the safety of using speech control operations. Taking ship control as the application background, a domain knowledge graph is established for ship control. Ship control commands are extracted from nautical books and classic naval warfare film and television materials, and a Chinese speech dataset for ship control commands is constructed. A domain knowledge-embedded decoding method is proposed to correct the output control commands by calculating the matching degree between the recognition result and the domain knowledge graph. Experimental results show that compared with the current popular connection time sequence classification decoding method and attention mechanism-based decoding method, the proposed decoding method reduces the word error rate by 4.0% and 1.5% when recognizing noisy speech with a signal-tonoise ratio of 10dB and 20dB, respectively, and improves the accuracy of command recognition by 10.3% and 6.3%, respectively, improving the robustness of the speech recognition model in recognizing Chinese commands. [ABSTRACT FROM AUTHOR]

To solve the problems that the Attention's overly flexible alignment was poorly adaptable in complex environments and the language features were not fully utilized by simple end-to-end models, a speech recognition method was investigated based on multi-task loss with additional language model. By analyzing the characteristics of the speech signal, the features containing more information were selected in the training. Based on the Attention-based Conformer end-to-end model, the model was trained using multi-task loss of CTC loss assisted pure Conformer (Attention), and the Conformer-CTC speech recognition model was obtained. Based on the Conformer-CTC model, by analyzing and comparing the characteristics and effects of some language models, the Transformer language model was added to the training of the above model through re-scoring mechanism, and the Conformer-CTC-Transformer speech recognition model was obtained. The experiments on the above model were completed on the AISHELL-1 data set. The results show that compared with the pure Conformer (Attention) model, the character error rate (CER) of the Conformer-CTC model on the test set is reduced by 0. 49%, and the CER of the Conformer-CTC-Transformer model on the test set is reduced by 0.79% compared with the Conformer- CTC model. The adaptability of Attention alignment in complex environments can be improved by CTC loss, and after re-scoring the Transformer-CTC model with the Transformer language model, the recognition accuracy can be increased by 0. 30% again. Compared with some existing end-to-end models, the recognition effect of the Conformer-CTC-Transformer model is better, indicating that the model has certain effectiveness. [ABSTRACT FROM AUTHOR]

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Speech recognition technology is a key component of the intelligent voice interaction system. In recent years, this technology has developed rapidly and is widely used in automotive electronics, consumer electronics, medical and other fields. This paper analyzes the development status of speech technology in the field of automotive field, introduces the development process of this technology in detail, focuses on the demand and application of speech recognition technology in the field of rail transit.Aiming at the problem that the display interactive content of different models of rail transit is changeable and needs to be customized and developed, the research on speech recognition technology based on time-delay neural network is carried out and a speech recognition system for train display based on intelligent computing platform is developed. The system can flexibly build models according to the interaction requirements of different display platforms and the processing performance of intelligent hardware platforms, and optimize the interaction keywords on the basis of the general model to improve the recognition rate. Finally, the test is completed in the voice interaction system of autonomous-rail rapid tram display in different scenes and noisy conditions and the word recognition rate reaches more than 85%. It also lays the foundation for the application of intelligent voice technology in other rail transit vehicles.

Speech emotion recognition(SER) refers to the use of machines to recognize the emotions of a speaker from speech.SER is an important part of human-computer interaction(HCI).But there are still many problems in SER research,e.g.,the lack of high-quality data,insufficient model accuracy,little research under noisy environments.In this paper,we propose a method called Head Fusion based on the multi-head attention mechanism to improve the accuracy of SER.We implemente an attention-based convolutional neural network(ACNN) model and conduct experiments on the interactive emotional dyadic motion capture(IEMOCAP) data set.The accuracy is improved to 76.18% (weighted accuracy,WA) and 76.36% (unweighted accuracy,UA).To the best of our knowledge,compared with the state-of-the-art result on this dataset(76.4% of WA and 70.1% of WA),we achieve a UA improvement of about 6% absolute while achieving a similar WA.Furthermore,We conduct empirical experiments by injecting speech data with 50 types of common noises.We inject the noises by altering the noise intensity,time-shifting the noises,and mixing different noise types,to identify their varied impacts on the SER accuracy and verify the robustness of our model.This work will also help researchers and engineers properly add their training data by using speech data with the appropriate types of noises to alleviate the problem of insufficient high-quality data.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, deep neural network model has become a hot research object in the field of speech recognition. However, the deep neural network relies on huge parameters and computational overhead, the excessively large model size also increases the difficulty of its deployment on edge devices. Aiming at the above problems, this paper proposed a lightweight speech recognition model based on Transformer. This method used depthwise separable convolution to obtain the feature information. Secondly, this method constructed a two half-step feed-forward layers, namely Macaron-N et, and introduced the lowrank matrix factorization to realize the model compression. Finally, it used a sparse attention mechanism to improve the training speed and decoding speed of the model. It tested on the Aishell-1 and aidatang_200zh datasets. The experimental results show that compared with Open-Transformer, the word error rate and real time factor of LM-Transformer decrease by 19. 8% and 32.1%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the problem that traditional sorting manipulator lacks perceptual ability, and most of them are rigid grasping, leading to inadequate adaptability of operation objects (such as fruits and other agricultural products), a multi-sensory flexible sorting system with auditory, visual and tactile functions was designed. Based on the structure and kinematics analysis of the connecting rod serial manipulator, the overall design scheme of the system was proposed. The hardware and software were designed for intelligent speech recognition module, vision module based on deep learning, pressure detection module with ATMEGA328P single chip microcomputer as the core controller. A sorting test was carried out with apple, kiwi, orange and pear as the target objects. The results show that the system can receive non-specific voice demands through human-machine interaction. And the robot arm moves and positions itself under the guidance of vision, grabs various target objects adaptively, and performs the sorting task accurately. The positioning accuracy of the system is 0.3 mm, and the average accuracy of sorting is 92%, which proves the system to be intelligent and flexible. The study provides reference for improving the intelligent level and environmental adaptability of the sorting system. [ABSTRACT FROM AUTHOR]

To study the influence of Chinese Pinyin on detecting and correcting text errors in speech recognition, a text proofreading model based on Chinese semantic and phonological information was proposed. Five Pinyin coding methods were designed to construct the character-Pinyin embedding vector that was employed as the input of the Seq2Seq model based on gated recurrent unit. At the same time, the attention mechanism was adopted to extract the Chinese semantic and phonological information of sentences to correct speech recognition errors. Aiming at the problem of insufficient labeled corpus, a data augmentation method was introduced, which could automatically obtain annotated corpora by exchanging the initials or finals of Chinese Pinyin. The experimental results on AISHELL-3's public data show that phonological information is conducive to the text proofreading model to detect and correct text errors after speech recognition, and the proposed data augmentation method can improve the error detection performance of the model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the past decade,end-to-end automatic speech recognition (ASR) frameworks have developed rapidly.End-to-end ASR has shown not only very different characteristics from traditional ASR based on hidden Markov models (HMMs),but also advanced performances.Thus,end-to-end ASR is being more and more popular and has become another major type of ASR frameworks.A keyword search (KWS) framework based on end-to-end ASR and frame-synchronous alignment is proposed for solving the problem that end-to-end ASR cannot provide accurate keyword timestamps and confidence scores,and experimental verification on a Vietnamese dataset is made.First,utterances are decoded by an end-to-end Uyghur ASR system,obtaining N-best hypotheses.Next,a dynamic programming-based alignment algorithm is implemented on each of these ASR hypotheses and per-frame phoneme probabilities,which are provided by a phoneme classifier jointly trained with the ASR model,to compute time stamps and confidence scores for each word in N-best hypotheses.Then,final KWS result is obtained by detecting keywords within N-best hypotheses and removing duplicated keyword occurrences according to time stamps and confident scores.Experimental results on a Vietnamese conversational telephone speech dataset show that the proposed KWS system achieves an F1 score of 77.6%,which is relatively 7.8% higher than the F1 score of the traditional HMM-based KWS system.The proposed system also provides reliable keyword confidence scores.

With the rapid development of multimedia and communication technology,the amount of multilingual speech data on the Internet is increasing.Speech recognition technology is the core for media analysis and processing.How to quickly expand from a few major languages such as Chinese and English to more languages has become a prominent issue yet to be overcome in order to improve multilingual processing capabilities.This article summarizes the latest progress in the field of acoustic model modeling,and discusses breakthroughs needed by traditional speech recognition technology in the course of moving from single language to multi-languages.The latest end-to-end speech recognition technology was exploited to construct a keyword spotting system,and the system achieves favorable performance.The approach is detailed as follows:1)multi-lingual hierarchical and structured acoustic model modeling method;2)multilingual acoustic modeling based on language classification information;3)end-to-end keyword spotting based on frame-synchronous alignments.

Speech is one of the ways of human-computer interaction, and speech recognition technology is an important part of artificial intelligence. In recent years, the application of neural network technology in the field of speech recognition has developed rapidly, and it has become the mainstream acoustic modeling technology in the field of speech recognition. However, there is a difference between target speaker's voice and training data in the test conditions, which leads to the problem of model incompatibility. Therefore, the speaker adaptation (SA) method is to solve the mismatch problem caused by the speaker difference, and the research on the speaker adaptation method has become a popular direction in the field of speech recognition. Compared with the speaker adaptation method in the traditional speech recognition system, the self-adaptation in the speech recognition system using neural network has the characteristics of huge model parameters and relatively small amount of data. Therefore, the speaker adaptation method in the neural network-based speech recognition system becomes a challenge. Firstly, this paper reviews the development history of the speaker adaptation method and the various problems encountered in the research of the neural network-based speaker adaptation method. Secondly, the speaker adaptation method is divided into the speaker adaptation method based on feature domain and the speaker adaptation method based on model domain. It also introduces the corresponding principles and improvement methods, and finally points out the pro-blems that still exist in the speaker adaptation method in speech recognition and the future development direction.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a new deep learning algorithm framework, Transformer has attracted more and more researchers?? attention and has become a current research hotspot. Inspired by humans focusing on important things only, the self-attention mechanism in the Transformer model mainly learns important information in the input sequence. For speech recogni-tion tasks, the focus is to transcribe the information of the input speech sequence into the corresponding language text. The past practice was to combine acoustic models, pronunciation dictionaries, and language models into a speech recognition system to achieve speech recognition tasks, while Transformer can integrate them into a single neural network to form an end-to-end speech recognition system, which solves the issues such as forced alignment and multi-module training of the traditional speech recognition system. Therefore, it is very necessary to discuss the problems of Transformer in speech recognition tasks. In this paper, the structure of the Transformer model is first introduced. Besides, the problems confronted by speech recognition are analyzed with respect to input speech sequence, deep model architecture, and model inference. Then the methods to solve the obstacles within the three aspects afore mentioned are outlined and summarized. Finally, the future application and direction of Transformer in speech recognition are concluded and prospected.

In a continuous speech recognition system, aiming at the complex environments (including the variability of speakers and environmental noise), the training data does not match the test data, which results in a low voice recognition rate. A speech recognition method based on adaptive deep neural network is studied. The improved regularized adaptive criterion and the adaptive deep neural network in the feature space are combined to improve data matching. The fusion of speaker identity vector i-vector and noise perception training are used to overcome speaker and environmental noise changes and improve the classification function of the output layer of the traditional deep neural network, which ensures the characteristics of compactness within the class and separation between classes. The test experiment was carried out by superimposing various background noises under the TIMIT English speech data set and the Microsoft Chinese speech data set. The results show that, compared with the current popular GMMHMM and traditional DNN speech acoustic models, our proposal decreases the recognition word error rate by 5.151% and 3.113% respectively, which improves the generalization performance and robustness of the model to a certain extent. [ABSTRACT FROM AUTHOR]

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In recent years, deep learning has made a breakthrough in the field of speech recognition, and pushes forward the wide application of speech recognition technology in people's daily lives. Further optimization of the speech recognition model needs to be supported by a larger scale calibrated data. However, the scale of the current open source audio data set is still too small, and corpus is mostly written language of news-based long texts. This paper, by talking about the popular speech recognition applications like human-computer interaction and intelligent customer service, builds and opens the largest ever Chinese Mandarin speech corpus DTZH1505 through crowdsourcing. Data set records natural speech of 6 408 speakers from 8 major Chinese dialect regions and 33 provinces, up to 1 505 hours and on various scenes like social networking, human-computer interaction, intelligent customer service and on-board commands. It can be widely used in the researches of corpus linguistics, conversation analysis, speech recognition, as well as speaker recognition. This paper implements a series benchmark speech recognition experiments, and the results show that: compared to the same scale Chinese speech corpus aishell2, the speech recognition model based on this data set has better performance. [ABSTRACT FROM AUTHOR]

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To improve the performance of multi-accent Mandarin speech recognition task,this paper presented a method for hybrid end-to-end automatic speech recognition(ASR) by combining CTC and multi-head attention by using a multiobjective training and joint decoding.The analysis shows that hybrid model with lower CTC weight and deeper encoder layers performance better learning capacity.And it trained a very deep models with up to 48 layers for encode-decoder architecture,which outperforms all previous end-to-end ASR approaches on Aidatatang 200 h multi-accent dataset,achieves 5.6% character error rate(CER) and 26.2% sentence error rate(SER).The experiment proves that the recognition rate of the end-to-end model proposed exceeds the general end-to-end model,and it has certain advancedness in solving the Mandarin recognition with accents. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the national network customer telephone voice recognition has poor recognition of core words in specific fields, this paper proposes a method based on HCLG domain weight enhancement and domain word correction, which can add domain words in real time and quickly, to dynamically optimize the language model and improve speech recognition. The model and algorithm are optimized in the various fields of the telephone voice consultation, maintenance, complaints, etc. of the State Grid Customer Service Center. The speech recognition results have been greatly improved.

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