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Journal articles on the topic 'Speaker recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sun, Linhui, Yunyi Bu, Bo Zou, Sheng Fu, and Pingan Li. "Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector." Electronics 10, no. 1 (2020): 20. http://dx.doi.org/10.3390/electronics10010020.

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Extracting speaker’s personalized feature parameters is vital for speaker recognition. Only one kind of feature cannot fully reflect the speaker’s personality information. In order to represent the speaker’s identity more comprehensively and improve speaker recognition rate, we propose a speaker recognition method based on the fusion feature of a deep and shallow recombination Gaussian supervector. In this method, the deep bottleneck features are first extracted by Deep Neural Network (DNN), which are used for the input of the Gaussian Mixture Model (GMM) to obtain the deep Gaussian supervecto

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2

Singh, Satyanand. "Forensic and Automatic Speaker Recognition System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2804. http://dx.doi.org/10.11591/ijece.v8i5.pp2804-2811.

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<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of

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3

Singh, Mahesh K., P. Mohana Satya, Vella Satyanarayana, and Sridevi Gamini. "Speaker Recognition Assessment in a Continuous System for Speaker Identification." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 862–67. http://dx.doi.org/10.37391/ijeer.100418.

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This research article presented and focused on recognizing speakers through multi-speaker speeches. The participation of several speakers includes every conference, talk or discussion. This type of talk has different problems as well as stages of processing. Challenges include the unique impurity of the surroundings, the involvement of speakers, speaker distance, microphone equipment etc. In addition to addressing these hurdles in real time, there are also problems in the treatment of the multi-speaker speech. Identifying speech segments, separating the speaking segments, constructing clusters

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4

Markowitz, Judith A. "Speaker recognition." Information Security Technical Report 3, no. 1 (1998): 14–20. http://dx.doi.org/10.1016/s1363-4127(98)80014-9.

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Markowitz, Judith A. "Speaker recognition." Information Security Technical Report 4 (January 1999): 28. http://dx.doi.org/10.1016/s1363-4127(99)80053-3.

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6

Furui, Sadaoki. "Speaker recognition." Scholarpedia 3, no. 4 (2008): 3715. http://dx.doi.org/10.4249/scholarpedia.3715.

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7

O'Shaughnessy, D. "Speaker recognition." IEEE ASSP Magazine 3, no. 4 (1986): 4–17. http://dx.doi.org/10.1109/massp.1986.1165388.

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8

Mannepalli, Kasiprasad, Suman Maloji, Panyam Narahari Sastry, Swetha Danthala, and Durgaprasad Mannepalli. "Text independent emotion recognition for Telugu speech by using prosodic features." International Journal of Engineering & Technology 7, no. 2.7 (2018): 594. http://dx.doi.org/10.14419/ijet.v7i2.7.10887.

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The human speech delivers different types of information about the speaker and speech. From the speech production side, the speech signal carries linguistic information such as the meaningful message and the language and emotional, geographical and the speaker’s physiological characteristics of the speaker information are conveyed. This paper focuses on automatically identifying the emotion of a speaker given a sample of speech. the speech signals considered in this work are collected from Telugu speakers. The features like pitch, pitch related prosody, energy and formants. The overall recogni

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9

Gonzalez-Rodriguez, Joaquin. "Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)." Loquens 1, no. 1 (2014): e007. http://dx.doi.org/10.3989/loquens.2014.007.

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Lakshmi Prasanna, P. "Attention for the speech of cleft lip and palate in speaker recognition." Open Journal of Pain Medicine 7, no. 1 (2023): 7–1. http://dx.doi.org/10.17352/ojpm.000036.

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Artificial Intelligence (AI) has become indispensable to all people, primarily for the purposes of speaker recognition, voice identification, educational purposes, workplace, and health care. Based on a speaker’s voice characteristics, identification and recognition of the speaker is accomplished. The voice is affected by both intra- and interspeaker variability. In addition to this, a condition known as structural abnormalities can cause resonance, which can seriously affect voice quality. As a result, speakers may experience difficulties when using AI-based devices. The study aims to investi

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11

Bouziane, Ayoub, Jamal Kharroubi, and Arsalane Zarghili. "Towards an Optimal Speaker Modeling in Speaker Verification Systems using Personalized Background Models." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3655. http://dx.doi.org/10.11591/ijece.v7i6.pp3655-3663.

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<p>This paper presents a novel speaker modeling approachfor speaker recognition systems. The basic idea of this approach consists of deriving the target speaker model from a personalized background model, composed only of the UBM Gaussian components which are really present in the speech of the target speaker. The motivation behind the derivation of speakers’ models from personalized background models is to exploit the observeddifference insome acoustic-classes between speakers, in order to improve the performance of speaker recognition systems.</p>The proposed approach was evaluat

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Ayoub, Bouziane, Kharroubi Jamal, and Zarghili Arsalane. "Towards an Optimal Speaker Modeling in Speaker Verification Systems using Personalized Background Models." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3655–63. https://doi.org/10.11591/ijece.v7i6.pp3655-3663.

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This paper presents a novel speaker modeling approachfor speaker recognition systems. The basic idea of this approach consists of deriving the target speaker model from a personalized background model, composed only of the UBM Gaussian components which are really present in the speech of the target speaker. The motivation behind the derivation of speakers’ models from personalized background models is to exploit the observeddifference insome acoustic-classes between speakers, in order to improve the performance of speaker recognition systems. The proposed approach was evaluatedfor speake

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13

Satyanand, Singh. "Forensic and Automatic Speaker Recognition System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2804–11. https://doi.org/10.11591/ijece.v8i5.pp2804-2811.

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Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, e-commerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithm-based system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or p

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14

Li, Tingyu. "Speaker Recognition System based on Triplet State Loss Function." Scientific Journal of Technology 5, no. 8 (2023): 39–46. http://dx.doi.org/10.54691/sjt.v5i8.5496.

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The purpose of this paper is to build a model and design a speaker recognition system by comprehensively summarizing and learning the research data of speaker speech recognition models at home and abroad, and adopting a research method based on deep machine learning theory. Its main contents and proposed methods are as follows: For data processing, firstly, select and download the public data set from official website, preprocess each voice in the data set, extract Fbank features, convert it into. npy, store it in a file, process the voice into a format suitable for model input, and wait for s

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15

Zong, Feng. "Speaker Recognition Techniques." Applied Mechanics and Materials 599-601 (August 2014): 1716–19. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1716.

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The speaker recognition technology and development of the basic concepts of history, lists and compares several commonly used feature extraction and pattern matching methods, summarize the current problems and its development were discussed.

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Campbell, J. P., W. Shen, W. M. Campbell, R. Schwartz, J. F. Bonastre, and D. Matrouf. "Forensic speaker recognition." IEEE Signal Processing Magazine 26, no. 2 (2009): 95–103. http://dx.doi.org/10.1109/msp.2008.931100.

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17

P.Mathiyalagan, Et al. "Deep Learning and Limited Boltzmann Machines for Speaker Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1 (2023): 247–50. http://dx.doi.org/10.17762/ijritcc.v11i1.9820.

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Speaker recognition has become an essential aspect of modern voice-based systems such as security and authentication applications. In this research, we propose a new method for speaker recognition based on deep learning and limited Boltzmann machines. The method comprises preemphasis and overlapping type framing, endpoint detection, feature extraction, and training of a depth belief network pattern using a limited Boltzmann machine layer. The Softmax graders are added in the top layer of the pattern, and the speaker's phonetic feature is input into the pattern for training. The likelihood prob

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18

Sukumar B. S. "Combined Speaker and Speech Ascent Recognition Aided Authentication Using Adaptive Convolutional Neural Network with Attention Mechanism." Journal of Electrical Systems 20, no. 10s (2024): 3254–82. https://doi.org/10.52783/jes.5759.

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Speaker recognition is a multimodal sensory technology that identifies people by their voices. Considering the safety and important usage of these types of systems, improved performance is critical (for example, voice-based transactions, control of access, and criminal detection). As voice recognition systems become integrated into future electronic devices, recognition of speakers will serve as an organic authentication method. Despite the fact they have already been available for over forty years, they aren't generally accepted as independent security measures due to their unacceptable inade

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19

Aung, Dr Zaw Win. "Automatic Attendance System Using Speaker Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 802–6. http://dx.doi.org/10.31142/ijtsrd18763.

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Dr., Zaw Win Aung. "Automatic Attendance System Using Speaker Recognition." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 802–6. https://doi.org/10.31142/ijtsrd18763.

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The main aim of this paper is to develop automatic attendance system using speaker recognition technique. The proposed system is software architecture which allows the user to access the system by making an utterance from microphone and the attendance of corresponding user is marked in the Microsoft Office Excel. The proposed system automates the whole process of taking attendance. The system uses text dependent open set speaker identification with MFCC features and vector quantization based speaker modeling for authenticating the user. A simple Euclidean distance scoring is used as the classi

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21

Lv, Gang, and Heming Zhao. "Joint Factor Analysis of Channel Mismatch in Whispering Speaker Verification." Archives of Acoustics 37, no. 4 (2012): 555–59. http://dx.doi.org/10.2478/v10168-012-0065-9.

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Abstract A speaker recognition system based on joint factor analysis (JFA) is proposed to improve whispering speakers’ recognition rate under channel mismatch. The system estimated separately the eigenvoice and the eigenchannel before calculating the corresponding speaker and the channel factors. Finally, a channel-free speaker model was built to describe accurately a speaker using model compensation. The test results from the whispered speech databases obtained under eight different channels showed that the correct recognition rate of a recognition system based on JFA was higher than that of

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22

Mridha, Muhammad Firoz, Abu Quwsar Ohi, Muhammad Mostafa Monowar, Md Abdul Hamid, Md Rashedul Islam, and Yutaka Watanobe. "U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data." Applied Sciences 11, no. 21 (2021): 10079. http://dx.doi.org/10.3390/app112110079.

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Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task. The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset. As the embedding systems are pre-trained, the performance of speaker recognition models greatly depends on domain adaptation policy, which may reduce if trained using inadequate

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23

Nursholihatun, Erina, Sudi Mariyanto Sasongko, and Abdullah Zainuddin. "IDENTIFIKASI SUARA MENGGUNAKAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENTS (MFCC) DAN JARINGAN SYARAF TIRUAN BACKPROPAGATION." DIELEKTRIKA 7, no. 1 (2020): 48. http://dx.doi.org/10.29303/dielektrika.v7i1.232.

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The voice is basic humans tool of communications. Speakers identifications is the process of recoqnizing the identity of a speaker by comparing the inputed voice features with all the features of each speaker in the database.There are two step of speaker identification process: feature extraction and pattern recognition. For the characteristic extraction phase using Mel Frequency Cepstrum Coefficient (MFCC) method. The method of pattern recognition using backpropagation artificial neural networks that compares the test data with the reference data in the database based on the variable result i

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Khadar Nawas, K., Manish Kumar Barik, and A. Nayeemulla Khan. "Speaker Recognition using Random Forest." ITM Web of Conferences 37 (2021): 01022. http://dx.doi.org/10.1051/itmconf/20213701022.

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Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output

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25

Tsai, Chia-Hung Dylan, Michael Hu, and Stone Cheng. "Enhancing the Recognition of Speakers in Different Distances using Voice Features." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 4 (2023): 4300–4306. http://dx.doi.org/10.3397/in_2023_0610.

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This paper proposes a method to enhance speaker recognition at varying distances by adjusting the reference voice based on voice features. Speaker recognition is the process of identifying an individual based on their voice. It involves analyzing and comparing various acoustic features of a person's voice with the reference voice in the database. Conventional speaker recognition techniques have limitations of reduced accuracy when speakers are from varying distances. In this work, we found that high-frequency signals tend to decline faster than low-frequency ones with respect to speaker distan

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Nematollahi, Mohammad Ali, and S. A. R. Al-Haddad. "Distant Speaker Recognition: An Overview." International Journal of Humanoid Robotics 13, no. 02 (2016): 1550032. http://dx.doi.org/10.1142/s0219843615500322.

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Distant speaker recognition (DSR) system assumes the microphones are far away from the speaker’s mouth. Also, the position of microphones can vary. Furthermore, various challenges and limitation in terms of coloration, ambient noise and reverberation can bring some difficulties for recognition of the speaker. Although, applying speech enhancement techniques can attenuate speech distortion components, it may remove speaker-specific information and increase the processing time in real-time application. Currently, many efforts have been investigated to develop DSR for commercial viable systems. I

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Leila, Beltaifa Zouari *1 Asma Chayeh 2. "SPEAKER RECOGNITION OF MAGHREB DIALECTS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 413–21. https://doi.org/10.5281/zenodo.1066198.

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A few studies have focused on the west Arabic (Maghreb) dialects for which resources are rare. To handle this problem, we devoped a web-based database of speech from Tunisian, Algerian and Moroccan speakers covering the diversity of Arabic dialects spoken in north Africa. Then speaker identification and verification experiments have been conducted in order to evaluate the performance of each dialect-based system. A baseline system using Timit database have also be developed for comparison purposes. The experiments show that the performances of dialectal speaker identification systems outperfor

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Chauhan, Neha, Tsuyoshi Isshiki, and Dongju Li. "Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies." Acoustics 6, no. 2 (2024): 439–69. http://dx.doi.org/10.3390/acoustics6020024.

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This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For i

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Huang, X., and K. F. Lee. "On speaker-independent, speaker-dependent, and speaker-adaptive speech recognition." IEEE Transactions on Speech and Audio Processing 1, no. 2 (1993): 150–57. http://dx.doi.org/10.1109/89.222875.

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Lee, Yun Kyung, and Jeon Gue Park. "Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech." Applied Sciences 11, no. 6 (2021): 2642. http://dx.doi.org/10.3390/app11062642.

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This paper addresses an automatic proficiency evaluation and speech recognition for second language (L2) speech. The proposed method recognizes the speech uttered by the L2 speaker, measures a variety of fluency scores, and evaluates the proficiency of the speaker’s spoken English. Stress and rhythm scores are one of the important factors used to evaluate fluency in spoken English and are computed by comparing the stress patterns and the rhythm distributions to those of native speakers. In order to compute the stress and rhythm scores even when the phonemic sequence of the L2 speaker’s English

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Singh, Mahesh K., S. Manusha, K. V. Balaramakrishna, and Sridevi Gamini. "Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 848–52. http://dx.doi.org/10.37391/ijeer.100415.

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The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector

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Xu, Hui Hong, and Su Chun Gao. "Speaker Recognition Study Based on Optimized Baum-Welch Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2471–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2471.

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The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.

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Thủy, Đào Thị Lệ, Trinh Van Loan, and Nguyen Hong Quang. "GMM FOR EMOTION RECOGNITION OF VIETNAMESE." Journal of Computer Science and Cybernetics 33, no. 3 (2018): 229–46. http://dx.doi.org/10.15625/1813-9663/33/3/11017.

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This paper presents the results of GMM-based recognition for four basic emotions of Vietnamese such as neutral, sadness, anger and happiness. The characteristic parameters of these emotions are extracted from speech signals and divided into different parameter sets for experiments. The experiments are carried out according to speaker-dependent or speaker-independent and content-dependent or content-independent recognitions. The results showed that the recognition scores are rather high with the case for which there is a full combination of parameters as MFCC and its first and second derivative

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34

Lim, Seunguook, and Jihie Kim. "SAPBERT: Speaker-Aware Pretrained BERT for Emotion Recognition in Conversation." Algorithms 16, no. 1 (2022): 8. http://dx.doi.org/10.3390/a16010008.

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Emotion recognition in conversation (ERC) is receiving more and more attention, as interactions between humans and machines increase in a variety of services such as chat-bot and virtual assistants. As emotional expressions within a conversation can heavily depend on the contextual information of the participating speakers, it is important to capture self-dependency and inter-speaker dynamics. In this study, we propose a new pre-trained model, SAPBERT, that learns to identify speakers in a conversation to capture the speaker-dependent contexts and address the ERC task. SAPBERT is pre-trained w

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Marini, Marco, Nicola Vanello, and Luca Fanucci. "Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria." Sensors 21, no. 19 (2021): 6460. http://dx.doi.org/10.3390/s21196460.

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Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the

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Kholiev, Vladislav O., and Olesia Yu Barkovska. "Improved Speaker Recognition System Using Automatic Lip Recognition." Control Systems and Computers, no. 1 (305) (2024): 38–49. http://dx.doi.org/10.15407/csc.2024.01.038.

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The paper is focused on the relevant problem of speech recognition using additional sources besides the voice itself, in conditions in which the quality or availability of audio information is inadequate (for example, in the presence of noise or additional speakers). This is achieved by using automatic lip recognition (ARL) methods, which rely on non-acoustic biosignals generated by the human body during speech production. Among the applications of this approach are medical applications, as well as processing voice commands in languages with poor audio conditions. The aim of this work is to cr

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Vishwakarma, Agrani. "Enhanced Speaker Recognition System." International Journal for Research in Applied Science and Engineering Technology 6, no. 5 (2018): 1544–50. http://dx.doi.org/10.22214/ijraset.2018.5251.

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Wang, William S. Y., and M. R. Schroeder. "Speech and Speaker Recognition." Language 62, no. 3 (1986): 706. http://dx.doi.org/10.2307/415501.

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Hanna, S. A., and Ann Stuart Laubstein. "Speaker‐independent sound recognition." Journal of the Acoustical Society of America 92, no. 4 (1992): 2475–76. http://dx.doi.org/10.1121/1.404442.

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Rodrigues, Luis, and John Kroeker. "Articulatory-based speaker recognition." Journal of the Acoustical Society of America 133, no. 5 (2013): 3300. http://dx.doi.org/10.1121/1.4805455.

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Campbell, J. P. "Speaker recognition: a tutorial." Proceedings of the IEEE 85, no. 9 (1997): 1437–62. http://dx.doi.org/10.1109/5.628714.

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Hickt, L. "Speech and speaker recognition." Signal Processing 13, no. 3 (1987): 336–38. http://dx.doi.org/10.1016/0165-1684(87)90137-x.

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Singh, Nilu, R. A. Khan, and Raj Shree. "Applications of Speaker Recognition." Procedia Engineering 38 (2012): 3122–26. http://dx.doi.org/10.1016/j.proeng.2012.06.363.

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Jin, Qin, Tanja Schultz, and Alex Waibel. "Far-Field Speaker Recognition." IEEE Transactions on Audio, Speech and Language Processing 15, no. 7 (2007): 2023–32. http://dx.doi.org/10.1109/tasl.2007.902876.

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Weychan, Radoslaw, Tomasz Marciniak, Agnieszka Stankiewicz, and Adam Dabrowski. "Real Time Recognition Of Speakers From Internet Audio Stream." Foundations of Computing and Decision Sciences 40, no. 3 (2015): 223–33. http://dx.doi.org/10.1515/fcds-2015-0014.

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Abstract In this paper we present an automatic speaker recognition technique with the use of the Internet radio lossy (encoded) speech signal streams. We show an influence of the audio encoder (e.g., bitrate) on the speaker model quality. The model of each speaker was calculated with the use of the Gaussian mixture model (GMM) approach. Both the speaker recognition and the further analysis were realized with the use of short utterances to facilitate real time processing. The neighborhoods of the speaker models were analyzed with the use of the ISOMAP algorithm. The experiments were based on fo

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Scruggs, Jeffrey L. "Speaker-dependent speech recognition using speaker independent models." Journal of the Acoustical Society of America 104, no. 5 (1998): 2558. http://dx.doi.org/10.1121/1.423803.

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Lee, Bong-Jin, Jeung-Yoon Choi, and Hong-Goo Kang. "Phonetically optimized speaker modeling for robust speaker recognition." Journal of the Acoustical Society of America 126, no. 3 (2009): EL100—EL106. http://dx.doi.org/10.1121/1.3204765.

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Misra, Hemant, Shajith Ikbal, and B. Yegnanarayana. "Speaker-specific mapping for text-independent speaker recognition." Speech Communication 39, no. 3-4 (2003): 301–10. http://dx.doi.org/10.1016/s0167-6393(02)00046-8.

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Kshirod, Kshirod Sarmah. "Speaker Diarization with Deep Learning Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2020): 2570–82. http://dx.doi.org/10.61841/turcomat.v11i3.14309.

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Speaker diarization is a task to identify the speaker when different speakers spoke in an audio or video recording environment. Artificial intelligence (AI) fields have effectively used Deep Learning (DL) to solve a variety of real-world application challenges. With effective applications in a wide range of subdomains, such as natural language processing, image processing, computer vision, speech and speaker recognition, and emotion recognition, cyber security, and many others, DL, a very innovative field of Machine Learning (ML), that is quickly emerging as the most potent machine learning te

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Gupta, Manish, Shambhu Shankar Bharti, and Suneeta Agarwal. "Gender-based speaker recognition from speech signals using GMM model." Modern Physics Letters B 33, no. 35 (2019): 1950438. http://dx.doi.org/10.1142/s0217984919504384.

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Abstract:

Speech is a convenient medium for communication among human beings. Speaker recognition is a process of automatically recognizing the speaker by processing the information included in the speech signal. In this paper, a new approach is proposed for speaker recognition through speech signal. Here, a two-level approach is proposed. In the first-level, the gender of the speaker is recognized, and in the second-level speaker is recognized based on recognized gender at first-level. After recognizing the gender of the speaker, search space is reduced to half for the second-level as speaker recogniti

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