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The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis. [ABSTRACT FROM AUTHOR]

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets. [ABSTRACT FROM AUTHOR]

In the era of advanced text-to-speech (TTS) systems capable of generating high-fidelity, human-like speech by referring a reference speech, voice cloning (VC), or zero-shot TTS (ZS-TTS), stands out as an important subtask. A primary challenge in VC is maintaining speech quality and speaker similarity with limited reference data for a specific speaker. However, existing VC systems often rely on naive combinations of embedded speaker vectors for speaker control, which compromises the capture of speaking style, voice print, and semantic accuracy. To overcome this, we introduce the Two-branch Speaker Control Module (TSCM), a novel and highly adaptable voice cloning module designed to precisely processing speaker or style control for a target speaker. Our method uses an advanced fusion of local-level features from a Gated Convolutional Network (GCN) and utterance-level features from a gated recurrent unit (GRU) to enhance speaker control. We demonstrate the effectiveness of TSCM by integrating it into advanced TTS systems like FastSpeech 2 and VITS architectures, significantly optimizing their performance. Experimental results show that TSCM enables accurate voice cloning for a target speaker with minimal data through both zero-shot or few-shot fine-tuning of pretrained TTS models. Furthermore, our TSCM-based VITS (TSCM-VITS) showcases superior performance in zero-shot scenarios compared to existing state-of-the-art VC systems, even with basic dataset configurations. Our method's superiority is validated through comprehensive subjective and objective evaluations. A demonstration of our system is available at https://great-research.github.io/tsct-tts-demo/, providing practical insights into its application and effectiveness. [ABSTRACT FROM AUTHOR]

In the whole power system, the importance of the transformer is self-evident, if its fault is bound to cause adverse effects on the whole power system. However, the previous fault diagnosis method is not only time-consuming and laborious, but also has great danger, once it is not handled properly, it may also pose a threat to the life safety of operators. At this time, the transformer fault diagnosis mode based on voice print follows and becomes a big tool for fault diagnosis. However, the surrounding environment of the transformer is changeable and noisy, which will cause interference to the voice print signal and affect the fault diagnosis result. In this regard, this paper will start from this key point and solve this unfavorable factor by using different denoising technologies and feature extraction technologies. The final empirical results show that the improved wavelet threshold technology has the best denoising ability, and the feature parameters obtained under the transformer sound frequency cepstrum coefficient (TFCC) feature extraction technology can better improve the fault diagnosis accuracy. It can solve the problem of low fault diagnosis rate of transformer in different environments, and can be used in practical engineering. [ABSTRACT FROM AUTHOR]

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers. [ABSTRACT FROM AUTHOR]

This work aims to further compensate for the weaknesses of feature sparsity and insufficient discriminative acoustic features in existing short-duration audio voiceprint recognition. To address this issue, we propose a Bark-scaled Gaussian and linear filter bank superposition cepstral coefficients (BGLCC), a multi-dimensional central difference (MDCD) acoustic feature, and a Fisher feature fusion of multi-source features. First, the rich low-frequency information is extracted based on the high distribution density of the Bark-scaled Gaussian filter bank in the low-frequency domain, and the extraction of more high-frequency information is based on the linear filter bank uniformly distributed in the high-frequency domain. In addition, the multidimensional central difference method captures better dynamic features of voiceprints in the relative BGLCC domain to improve the performance of short utterance speaker recognition. Finally, the Fisher feature fusion method (FFF) can further enhance speaker individual information and reduce speaker commonality information. Extensive experiments are conducted on short-duration text-independent speaker verification datasets generated from the VoxCeleb corpus, which speech samples of diverse lengths. The results demonstrate that the proposed method outperforms the existing acoustic feature extraction approach by at least 15% in the test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods. [ABSTRACT FROM AUTHOR]

This essay addresses the status of voice and print in dialect poetry through a study of its mid-nineteenth-century heyday in British literature. Dialect poems are often imagined to render oral forms into print, so that their writtenness appears secondary and belated. This essay argues differently, proposing that the Victorian poets it considers—William Barnes, Janet Hamilton, and Ralph Ditchfield—instead develop original forms of expression from the interaction of speech and text. Rather than needing to be liberated from print by voice, Victorian dialect poems play on the difficulty of their being spoken, thriving on the difference between types of language and varieties of audience. [ABSTRACT FROM AUTHOR]

Some of the earliest voice recordings of Arabic made for linguistic purposes date from World War I and were made by German authorities who recorded prisoners of war in the Halbmondlager camp outside of Berlin. This study analyzes two voice records in particular, which are labelled 'Tripolitanisch-Arabisch (Tunesien)' and stem from what is now southern Tunisia. This study seeks to historicise the scientific context of these voice records as well as interpret the linguistic data preserved by them in the light of Arabic dialectology. It also raises questions about the reliability of both voice recordings and printed linguistic data from the time. [ABSTRACT FROM AUTHOR]

With the advantages of realtime analysis and visual evaluation results, intelligent technology-enabled teaching behavior evaluation has gradually become a powerful means to help teachers adjust teaching behaviors and improve teaching quality. However, at present, the evaluation of intelligent teachers' behaviors is still in the preliminary exploration stage, and the application research is not deep enough. This paper analyzes the application of intelligent technology in the evaluation of teachers' classroom teaching behaviors from the perspectives of evaluation data, methods, and results. Voice print recognition technology is used to recognize the teachers' identities and track the speech in the classroom videos, and the videos are segmented. Then, the evaluation framework of teachers' classroom teaching behaviors is constructed using three dimensions of emotion, posture, and position preference. Finally, evaluation results are presented to teachers in a more intuitive and easy-to-understand visual way, to help teachers reflect on teaching. This paper aims to promote the transformation of teachers' classroom teaching behavior evaluation toward an intelligent, efficient, and sustainable direction through current research. [ABSTRACT FROM AUTHOR]

The traditional power transformer diagnosis method relies on a lot of experience knowledge and a complex sampling process, which brings great difficulties to the fault diagnosis work. To solve this problem, a fault feature extraction method based on fully adaptive noise set empirical mode decomposition (CEEMDAN) is proposed, and the hunter–prey optimization (HPO) algorithm is used to optimize the support vector machine (SVM) to identify and classify the voice print faults of power transformers. Firstly, the CEEMDAN algorithm is used to decompose the voicemarks into several IMF components. IMF components containing fault information are selected according to the envelope kurtosis index and reconstructed to generate new signal sequences. PCA dimensionality reduction is performed on the reconstructed signal, and the principal components are extracted with a high cumulative contribution rate as input to SVM. Then, the HPO-SVM algorithm is used to classify and identify transformer faults. Apply the proposed method to the diagnosis of typical faults in power transformers. The results show that the accuracy of this method in identifying various fault states of power transformers can reach 98.5%, and it has better classification performance than other similar methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of College of Languages is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Intelligent speech recognition is increasingly used in embedded systems, which is also seriously threatened by malicious speech spoofing attacks. Different from the conventional methods, this article proposes a segment-based anti-spoofing detection (SASD) method for the quick detection of spoofed speeches against embedded speech recognition, which focuses on the anti-spoofing features rather than the contexts of speeches and the voiceprints of speakers. The speeches are divided into word segments and silent segments. Based on constant $Q$ cepstral coefficients (CQCCs), a word CQCC (WCQCC) extraction is first designed for the word segments of speeches. Then, based on short-term zero crossing rate (ZCR), an average ZCR (AZCR) extraction is devised for the silent segments. Combining the WCQCC of word segments and AZCR of silent segments, a biased decision strategy is proposed to quickly determine whether a speech is spoofed. Based on ASVspoof 2021 datasets, extensive experiments are conducted to evaluate the effectiveness of the proposed method. Specifically, our SASD can improve the accuracy of anti-spoofing detection by up to 33.47% and save up to 69.10% of time overhead on embedded devices compared with the existing methods. [ABSTRACT FROM AUTHOR]

This paper is an attempt to address to the problem of native language in a mixed voice environment. G- Cocktail would aid these applications in identifying commands given in Gujarati, even from a mixed voice stream. There are two phases of G-cocktail in the first phase, it creates features after filtering the voices and in the second it trains and classifies the dataset. This trained dataset helps in recognizing the new voice signal. The challenge in training a native language is the availability of a small dataset. A single-word input is used in model and phrase benchmark dataset from Microsoft and the Linguistic Data Consortium for Indian Languages (LDC-IL). To overcome the over fitting problem due to smaller dataset we used CatBoost algorithm. And fine-tuned the classification model to avoid the over fitting issue. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived from a type of cepstral representation of the audio clip (a nonlinear "spectrum-of-a-spectrum"). MFCC is good for human voices but noises in the sound makes it less productive. To avoid this shortcoming of MFCC, first filtered the voices are used and then calculated the MFCC. The most relevant features are retained to make it more robust. With MFCC features, the pitch of the voices is also added, as pitch could vary with regional changes, mood of the person, age, and knowledge of the language to the speaker. A voice print of the whole sound files is constructed and fed it as features to the classification model. For training and testing 70% and 30% ratio is used in algorithms like K-means, Naïve Bayes, and Light GBM. Proposed model is compared with given data set and results proved that G-cocktail using XBoost performed better than the others under the given scenario in all parameters. [ABSTRACT FROM AUTHOR]

Energy harvesting technologies now play a significant role in the successful deployment of self-powered electronic devices. Researchers are working on small-scale energy generators fabricated with nanomaterials to harvest ambient energy. Triboelectric nanogenerator (TENG) is an efficient method for harvesting mechanical energy and powering battery-less tiny devices for wearable, implantable medical sensing and internet of things (IoT) sensing applications. So far, many fabrication technologies have been discussed in the literature for the fabrication of TENG including traditional micro/nano-fabrication technologies and 3D printing or additive manufacturing technologies. Extrusion-based 3D printing is a reliable approach for developing a fast, economical and controllable TENG device. This article provides a detailed analysis of recently used material combinations, design and structure formation, and output performance of extrusion-based 3D-printed triboelectric nanogenerators (EB-3DP-TENGs). Also, it presents their latest applications, including powering of electronic devices, silent speech recognition, voice print sensing and gait monitoring. Moreover, it discusses the crucial challenges and approaches used to enhance the performance efficiency of EB-3DP-TENGs. Finally, a visionary roadmap for the future development of EB-3DP-TENGs is provided, which will accelerate research and development. [ABSTRACT FROM AUTHOR]

The identical twins (monozygotic) are siblings (sib) created from the division of one fertilized egg (zygote), so they will be identical in their genetic characteristics and therefore in their phenotypic traits to a very large extent. The voiceprint of the twins is one of these traits. This paper proposed a method to determine the extent of the similarity and dis-similarity between the voiceprints for the brothers of identical twin and thus it is possible to distinguish between their voices. The proposed method adopts the use of the spectrogram as two-dimensional function with neutrosophic transformation. The neutrosophy theory is applied to all spectrogram elements that belong to the first sibling to determine to what extent this element belongs to the spectrogram of the second sibling for the same utterance. This study relied on the use of a number of voice clips collected from 35 identical twins. The neutrosophic transformation is used to convert the voiceprints into the neutrosophic domain represented by three membership functions (true, false, and indeterminate). The results showed that the average extent of the similarity ratio between twins' voices (true membership) is 68.15%, the difference ratio (false membership) is 31.84%, and the indeterminacy membership function ratio is 18.83%. [ABSTRACT FROM AUTHOR]

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously generated and shared, and it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before it is shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also discuss their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research. [ABSTRACT FROM AUTHOR]

The article is an editor's message from the journal VOICEPrints. The editor expresses gratitude for the opportunity to serve as Editor-in-Chief and acknowledges the hard work of the team and the Board of Directors. The issue contains two feature articles: one reflecting on a career in New York in the late 1960s and the other discussing the use of jazz in private studios. The editor also announces the appointment of Ronald Land as the new Editor-in-Chief. [Extracted from the article]

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.)

Speaker recognition (SR) or identification is the subset of broad area of Pattern recognition. Given the features of the voice print, the recognition system identifies the speaker from the knowledge of the speaker models stored in the database. In today's world when many of our works are done through voice, recognition of the speaker is necessary.Recently, SR has also gained importance in Internet of Things (IoT) like setting up of smart environments for home, industries or educational and commercial applications. The race for high accuracy needs making the devices used in these smart environments as close to human hearing capacity as possible. Speaker identification is mostly used to establish negative recognition [1].Negative recognition is when the system decides whether a person is who he disagrees to be thus preventing a person from exploiting multiple identities. Only biometrics will be suitable to establish such identification. The feature extraction of voice sample along with comparative analysis of its methods is of fundamental interest in this paper. We try to compare the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients(MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices. [ABSTRACT FROM AUTHOR]

The article discusses the need to fill two administrative board positions in the New York Singing Teachers Association (NYSTA) - the VOICEPrints Editor-in-Chief and the Events Coordinator. The current Editor-in-Chief will be stepping down in June 2024, and the association is seeking a replacement as soon as possible. They are also looking for an Events Coordinator to take over responsibilities from someone who is currently unable to fulfill the role. The article emphasizes the importance of diversifying the board by seeking individuals outside the current majority demographics. Interested individuals are encouraged to contact the association by April 1st. [Extracted from the article]

The article titled "Programmable Polymeric-Interface for Voiceprint Biometrics" published in Advanced Materials Technologies discusses a new development in voiceprint biometrics. The authors, Anand Babu, Damien Thuau, and their colleagues, introduce a programmable polymeric interface that utilizes artificial intelligence to accurately capture and analyze voice signatures. This technology enhances the existing landscape of biometrics and provides a valuable tool for voice recognition. [Extracted from the article]

Automatic speaker recognition applications have often been described as a 'black box'. This study explores the benefit of tuning procedures (condition adaptation and reference normalisation) implemented in an i-vector PLDA framework ASR system, VOCALISE. These procedures enable users to open the black box to a certain degree. Subsets of two 100-speaker databases, one of Czech and the other of Persian male speakers, are used for the baseline condition and for the tuning procedures. The effect of tuning with cross-language material, as well as the effect of simulated voice disguise, achieved by raising the fundamental frequency by four semitones and resonance characteristics by 8%, are also examined. The results show superior recognition performance (EER) for Persian than Czech in the baseline condition, but an opposite result in the simulated disguise condition; possible reasons for this are discussed. Overall, the study suggests that both condition adaptation and reference normalisation are beneficial to recognition performance. [ABSTRACT FROM AUTHOR]

The main novelty of this work resides in incorporating a Gammatone filter-bank as a substitute of the Mel filter-bank in the extraction pipeline of the Product Spectrum PS. The proposed feature is dubbed the Gammatone Product-Spectrum Cepstral coefficients GPSCC. Experimental results are undertaken on TIMIT and noisy TIMIT corpora using the Gaussian Mixture Model with Universal Background Model (GMM-UBM) recognition algorithm. Performance evaluations indicate that GPSCC shows a drastic reduction in Equal Error Rates compared to other related features and this gain in performance is more pronounced at low signal to noise ratios. Also, our study demonstrates the merit of the Gammatone filter-bank in improving robustness to codec-degraded speech at different bit rates. Furthermore, the proposed GPSCC feature achieves the best verification performance under aggressive compression. Interestingly, at 6.60 kbps we observe that GPSCC achieves an absolute error reduction of 12% compared to the Mel Frequency Cepstral Coefficients (MFCC). [ABSTRACT FROM AUTHOR]

Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks. Anti-spoofing, determining whether a speech signal is natural/genuine or spoofed, is very important for improving the reliability of the ASV systems. Spoofing attacks using the speech signals generated using speech synthesis and voice conversion have recently received great interest due to the 2015 edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). In this paper, we propose to use linear prediction (LP) residual based features for anti-spoofing. Three different features extracted from LP residual signal were compared using the ASVspoof 2015 database. Experimental results indicate that LP residual phase cepstral coefficients (LPRPC) and LP residual Hilbert envelope cepstral coefficients (LPRHEC) obtained from the analytic signal of the LP residual yield promising results for anti-spoofing. The proposed features are found to outperform standard Mel-frequency cepstral coefficients (MFCC) and Cosine Phase (CosPhase) features. LPRPC and LPRHEC features give the smallest equal error rates (EER) for eight spoofing methods out of ten spoofing attacks in comparison to MFCC and CosPhase features. [ABSTRACT FROM AUTHOR]

Enhancing authentication and authorization with biometrics improves identification and validation of users. What is more valuable, however, is gaining visibility and knowledge about an authenticated user to reduce risks against insider threats and compromised credentials. To effectively do so, the implementation of behavioral biometrics is recommended. Conversely, it's essential for organizations to understand their ethical responsibilities to ensure proper use and preserve the security of the data that has been collected and correlated from biometric implementations. [ABSTRACT FROM AUTHOR]

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features. [ABSTRACT FROM AUTHOR]

The present work describes different research techniques for collecting and organizing speech database in different scenario at the institute and successfully structuring the text independent speaker identification database in Indian context. In order to get the Multi-Scenario dataset, each speaker performed multiple sessions recording in reading style with English and Hindi language with same passages but under different conditions. This work analyzed different scenario affecting the performance of speaker recognition system when tested under dissimilar training conditions. Here four different scenarios are considered; sensor and environment, language, aging and health. To study the effect of sensor, language and environment on the performance of ASR system a database of 200 speaker was created. Under different environmental conditions, four different types of sensors in parallel configuration were used to study the sensor mismatch conditions over testing and training phase. The database contains speech samples of the individual in English and Hindi in read speech styles under two environment i.e. a controlled recording chamber and library. To study the aging effect, an aging NSIT speaker database (AG-NSIT-SD) of 53 famous personalities was collected from online source varying over a period of 10–20 years. Further to study the effect of health, a cough and cold NSIT speaker database (CC-NSIT-SD) of 38 speakers was also collected to study the performance of system. Apart from this, the effect of different noise types on the speaker identification was also studied on different sensors. [ABSTRACT FROM AUTHOR]

Speaker verification (SV) systems involve mainly two individual stages: feature extraction and classification. In this paper, we explore these two modules with the aim of improving the performance of a speaker verification system under noisy conditions. On the one hand, the choice of the most appropriate acoustic features is a crucial factor for performing robust speaker verification. The acoustic parameters used in the proposed system are: Mel Frequency Cepstral Coefficients, their first and second derivatives (Deltas and Delta–Deltas), Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive, and Relative Spectral Transform Perceptual Linear Predictive. In this paper, a complete comparison of different combinations of the previous features is discussed. On the other hand, the major weakness of a conventional support vector machine (SVM) classifier is the use of generic traditional kernel functions to compute the distances among data points. However, the kernel function of an SVM has great influence on its performance. In this work, we propose the combination of two SVM-based classifiers with different kernel functions: linear kernel and Gaussian radial basis function kernel with a logistic regression classifier. The combination is carried out by means of a parallel structure approach, in which different voting rules to take the final decision are considered. Results show that significant improvement in the performance of the SV system is achieved by using the combined features with the combined classifiers either with clean speech or in the presence of noise. Finally, to enhance the system more in noisy environments, the inclusion of the multiband noise removal technique as a preprocessing stage is proposed. [ABSTRACT FROM AUTHOR]

Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system using Mel-frequency Cepstral Coefficient (MFCC) and Gaussian Mixture Model (GMM). In this paper, a database of five Quranic reciters is developed and used in training and testing phases. We carefully randomized the database from various surah in the Quran so that the proposed system will not prone to the recited verses but only to the reciter. Around 15 Quranic audio samples from 5 reciters were collected and randomized, in which 10 samples were used for training the GMM and 5 samples were used for testing. Results showed that our proposed system has 100% recognition rate for the five reciters tested. Even when tested with unknown samples, the proposed system is able to reject it. [ABSTRACT FROM AUTHOR]

Speaker Recognition is a vital application of speech processing. Speaker Recognition performs a task of authenticating or recognizing a speaker based on the unique features captured which characterize the speaker. Characteristics or features which are unique to an individual such as fundamental frequency, speaking style, pitch, and duration are used as distinguishing components of the human speech signal. Exploring these characteristics for various applications with an attempt to implement a robust speaker recognition system has been the impetus behind the research in this domain. This paper makes an attempt to present the available Feature Extraction and Recognition techniques with their merits and demerits. It also discusses the pre-emphasis stage of the speaker recognition system. The standard databases available for speaker recognition along with the criterion for their selection are also reviewed. The paper presents an overview of various toolkits and performance parameters of Automatic Speaker Recognition System. [ABSTRACT FROM AUTHOR]