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Journal articles on the topic 'Speaker identification system'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Aung, Zaw Win. "A Robust Speaker Identification System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2057–64. http://dx.doi.org/10.31142/ijtsrd18274.

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Jayanna, H. S., and B. G. Nagaraja. "An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification." Journal of Intelligent Systems 25, no. 4 (2016): 529–38. http://dx.doi.org/10.1515/jisys-2014-0128.

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AbstractMost of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in ma

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Kim, Kyung-Wha, Byung-Min So, and Ha-Jin Yu. "Forensic Automatic Speaker Identification System for Korean Speakers." Phonetics and Speech Sciences 4, no. 3 (2012): 95–101. http://dx.doi.org/10.13064/ksss.2012.4.3.095.

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Shah, Shahid Munir, Muhammad Moinuddin, and Rizwan Ahmed Khan. "A Robust Approach for Speaker Identification Using Dialect Information." Applied Computational Intelligence and Soft Computing 2022 (March 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/4980920.

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The present research is an effort to enhance the performance of voice processing systems, in our case the speaker identification system (SIS) by addressing the variability caused by the dialectical variations of a language. We present an effective solution to reduce dialect-related variability from voice processing systems. The proposed method minimizes the system’s complexity by reducing search space during the testing process of speaker identification. The speaker is searched from the set of speakers of the identified dialect instead of all the speakers present in system training. The study

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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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Zaw, Win Aung. "A Robust Speaker Identification System." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2057–64. https://doi.org/10.31142/ijtsrd18274.

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This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text dependent speaker identification system. It contains three main modules endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short Term Energy analysis is used for endpoint detection. Mel frequency Cepstrum Coefficients MFC

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Mammone, Richard J. "Speaker identification and verification system." Journal of the Acoustical Society of America 101, no. 2 (1997): 665. http://dx.doi.org/10.1121/1.419408.

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8

Kleider, John Eric, and Khaled Assaleh. "Speaker identification system and method." Journal of the Acoustical Society of America 107, no. 6 (2000): 2952. http://dx.doi.org/10.1121/1.429392.

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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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10

EhKan, Phaklen, Timothy Allen, and Steven F. Quigley. "FPGA Implementation for GMM-Based Speaker Identification." International Journal of Reconfigurable Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/420369.

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In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors su

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11

Almarshady, Nourah M., Adal A. Alashban, and Yousef A. Alotaibi. "Analysis and Investigation of Speaker Identification Problems Using Deep Learning Networks and the YOHO English Speech Dataset." Applied Sciences 13, no. 17 (2023): 9567. http://dx.doi.org/10.3390/app13179567.

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The rapid momentum of deep neural networks (DNNs) in recent years has yielded state-of-the-art performance in various machine-learning tasks using speaker identification systems. Speaker identification is based on the speech signals and the features that can be extracted from them. In this article, we proposed a speaker identification system using the developed DNNs models. The system is based on the acoustic and prosodic features of the speech signal, such as pitch frequency (vocal cords vibration rate), energy (loudness of speech), their derivations, and any additional acoustic and prosodic

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Khoma, Volodymyr, Yuriy Khoma, Vitalii Brydinskyi, and Alexander Konovalov. "Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library." Sensors 23, no. 4 (2023): 2082. http://dx.doi.org/10.3390/s23042082.

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Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are

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INOUE, Yoshiaki, and Satoshi KUMAKURA. "Working over Speech : Speaker Verification System "VoiceGATEII", and Speaker Identification System "VoiceSync"." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2000 (2000): 1–4. http://dx.doi.org/10.1299/jsmeiip.2000.1.

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Bakst, Sarah, Ebony Pearson, Luciana Ferrer, Mitchell McLaren, and Aaron Lawson. "A phonetic basis of accent bias in speaker identification technology." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A103. https://doi.org/10.1121/10.0035253.

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Speaker identification (SID) technology aims to determine whether the speech in a recording of an unknown speaker matches that of speaker known to the SID system. If the unknown speaker comes from a community that is underrepresented in the SID training data (e.g., accent), the SID model is more likely to confuse the unknown speaker for other speakers in that speaker community: group-level characteristics are mistaken for individual identifiers (all speakers with that accent seem like the same speaker to SID). Previous solutions include reweighting training data to create balance across speake

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., Shweta Bansal. "SPEAKER IDENTIFICATION SYSTEM USING CLOSE SET." International Journal of Research in Engineering and Technology 01, no. 03 (2012): 411–14. http://dx.doi.org/10.15623/ijret.2012.0103038.

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Kim, Min-Joung, Soo-Young Suk, and Jong-Hyeog Jeong. "A Speaker Pruning Method for Real-Time Speaker Identification System." IEMEK Journal of Embedded Systems and Applications 10, no. 2 (2015): 65–71. http://dx.doi.org/10.14372/iemek.2015.10.2.65.

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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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18

Bennani, Younès. "A Modular and Hybrid Connectionist System for Speaker Identification." Neural Computation 7, no. 4 (1995): 791–98. http://dx.doi.org/10.1162/neco.1995.7.4.791.

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This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, and allowing a priori knowledge to be incorporated into their design. Text-independent speaker identification is an inherently complex task where the amount of training data is often limited. It thus provides an ideal domain to test the validity of the modular/hybrid connectionist approach. To achieve such identification, we develop, in this paper, an architecture based upon the cooperation of se

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Alkhatib, Bassel, and Mohammad Madian Waleed Kamal Eddin. "Voice Identification Using MFCC and Vector Quantization." Baghdad Science Journal 17, no. 3(Suppl.) (2020): 1019. http://dx.doi.org/10.21123/bsj.2020.17.3(suppl.).1019.

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The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker ident

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Kamiński, Kamil A., and Andrzej P. Dobrowolski. "Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features." Sensors 22, no. 23 (2022): 9370. http://dx.doi.org/10.3390/s22239370.

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This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known cer

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Hesham, A. Alabbasi, M. Jalil Ali, and S. Hasan Fadhil. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5208–16. https://doi.org/10.11591/ijece.v10i5.pp5208-5216.

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a

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22

Bhavikatti, Sharath B., H. N. Sharada, and S. S. Vannal. "Speaker Identification System in Controlled Environment using MFCC and VQ Technique." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 121–25. http://dx.doi.org/10.9756/bijsesc.8257.

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23

Deepak Chouhan,T Aryan Raju, Vaidehi Pradeep Gade et al. "Language-Independent Speaker Identification System-based Attendance System." Multimedia Research 5, no. 3 (2022): 18–24. http://dx.doi.org/10.46253/j.mr.v5i3.a3.

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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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25

Alabbasi, Hesham A., Ali M. Jalil, and Fadhil S. Hasan. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5208. http://dx.doi.org/10.11591/ijece.v10i5.pp5208-5216.

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a

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Mohammed, Rawia A., Nidaa F. Hassan, and Akbas E. Ali. "Arabic Speaker Identification System Using Multi Features." Engineering and Technology Journal 38, no. 5A (2020): 769–78. http://dx.doi.org/10.30684/etj.v38i5a.408.

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The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is

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Mr., Ajinkya N. Jadhav, and N. V. Dharwadkar Dr. "A SPEAKER RECOGNITION SYSTEM USING GAUSSIAN MIXTURE MODEL." JournalNX - a Multidisciplinary Peer Reviewed Journal RIT PG Con-18 (April 22, 2018): 120–25. https://doi.org/10.5281/zenodo.1413325.

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Automatic speaker recognition system identifies a person from the information contained in the speech signal. These systems are the most user-friendly means of biometric recognition and are being used in applications like teleconferencing, banking, forensics etc. https://journalnx.com/journal-article/20150502

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Madhusudhana Rao, T. V., Suribabu Korada, and Y. Srinivas. "Machine hearing system for teleconference authentication with effective speech analysis." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 3 (2021): 357–65. http://dx.doi.org/10.3233/kes-210079.

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The speaker identification in Teleconferencing scenario, it is important to address whether a particular speaker is a part of a conference or not and to note that whether a particular speaker is spoken at the meeting or not. The feature vectors are extracted using MFCC-SDC-LPC. The Generalized Gamma Distribution is used to model the feature vectors. K-means algorithm is utilized to cluster the speech data. The test speaker is to be verified that he/she is a participant in the conference. A conference database is generated with 50 speakers. In order to test the model, 20 different speakers not

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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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Li, Qiang, and Yan Hong Liu. "SVM-GMM Based Speaker Identification." Advanced Materials Research 1044-1045 (October 2014): 1370–74. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1370.

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Although a great success has been achieved under the environment of lab where the training data is sufficient and the surroundings are quiet, speaker identification (SI) in practical use still remains a challenge because of the complicated environment. To tackle this challenge, a hybrid system of Gaussian mixture model-support vector machines (GMM-SVM) is proposed in this paper. SVM can do well with less data but is computationally expensive while GMM is computationally inexpensive but needs more data to perform adequately. In this paper, SVM and GMM are parallel in both the training and testi

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Mamun, Nursadul, Ria Ghosh, and John H. L. Hansen. "Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users." Journal of the Acoustical Society of America 153, no. 2 (2023): 1293–306. http://dx.doi.org/10.1121/10.0017216.

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In the area of speech processing, human speaker identification under naturalistic environments is a challenging task, especially for hearing-impaired individuals with cochlear implants (CIs) or hearing aids (HAs). Motivated by the fact that electrodograms reflect direct CI stimulation of input audio, this study proposes a speaker identification (ID) investigation using two-dimensional electrodograms constructed from the responses of a CI auditory system to emulate CI speaker ID capabilities. Features are extracted from electrodograms through an identity vector (i-vector) framework to train and

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Gorban, Igor I. "Crime automatic speaker verification and identification system." Journal of the Acoustical Society of America 102, no. 5 (1997): 3165. http://dx.doi.org/10.1121/1.420769.

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Hassan, Tariq A., Eman K. Ibrahim, and Ziad M. Abood. "Genetic Algorithm Filtering for Speaker Identification System." International Journal for Sciences and Technology 12, no. 1 (2017): 15–20. http://dx.doi.org/10.12816/0040714.

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Ahmed, Benyamina, Benkrama Soumia, and Mohammed Yazid Bentaib. "Optimizing speaker identification: a comprehensive study with deep neural networks." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e7547. http://dx.doi.org/10.54021/seesv5n2-169.

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This article investigates the development and evaluation of a speaker identification system using deep learning techniques, with a focus on Convolutional Neural Networks (CNNs) and the audioMNIST dataset. The study reveals significant advancements in speaker identification, demonstrating substantial improvements over state-of-the-art models. Our system achieves high accuracy and reliability in distinguishing speakers, showcasing its potential applications in forensic science, security, and privacy protection. The paper thoroughly examines audio signal representation, preprocessing techniques,

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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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Kumar Pentapati, Hema, and Sridevi K. "Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network." International journal of electrical and computer engineering systems 14, no. 3 (2023): 301–10. http://dx.doi.org/10.32985/ijeces.14.3.8.

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There are various challenges in identifying the speakers accurately. The Extraction of discriminative features is a vital task for accurate identification in the speaker identification task. Nowadays, speaker identification is widely investigated using deep learning. The complex and noisy speech data affects the performance of Mel Frequency Cepstral Coefficients (MFCC); hence, MFCC fails to represent the speaker characteristics accurately. In this proposed work, a novel text-independent speaker identification system is developed to enhance the performance by fusion of Log-MelSpectrum and excit

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Youssef, Karim, Katsutoshi Itoyama, and Kazuyoshi Yoshii. "Simultaneous Identification and Localization of Still and Mobile Speakers Based on Binaural Robot Audition." Journal of Robotics and Mechatronics 29, no. 1 (2017): 59–71. http://dx.doi.org/10.20965/jrm.2017.p0059.

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[abstFig src='/00290001/06.jpg' width='300' text='Efficient mobile speaker tracking' ] This paper jointly addresses the tasks of speaker identification and localization with binaural signals. The proposed system operates in noisy and echoic environments and involves limited computations. It demonstrates that a simultaneous identification and localization operation can benefit from a common signal processing front end for feature extraction. Moreover, a joint exploitation of the identity and position estimation outputs allows the outputs to limit each other’s errors. Equivalent rectangular band

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Musab, T. S. Al-Kaltakchi, Abd Al-Raheem Taha Haithem, Abd Shehab Mohanad, and A. M. Abdullah Mohammed. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 782–89. https://doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight dif

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Li, Qiang, Ming Bing Zhao, and Yong Feng. "Speaker Identification in Total Variability Space." Applied Mechanics and Materials 401-403 (September 2013): 1489–92. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1489.

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Gaussian Mixture Model-Universal Background Model based approaches have been popular used for speaker identification task. But in real complex environment the identification system performs too much worse than in laboratory, and the main reason is the mismatch of the training and testing channel and also the variability of the speaker himself. In this paper we introduce i-vector to the speaker identification system. In i-vector approach, a low dimensional subspace called total variability space is used to estimate both speaker and channel variability. Baum-Welch statistics are first computed o

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BENNANI, YOUNÈS. "MULTI-EXPERT AND HYBRID CONNECTIONIST APPROACH FOR PATTERN RECOGNITION: SPEAKER IDENTIFICATION TASK." International Journal of Neural Systems 05, no. 03 (1994): 207–16. http://dx.doi.org/10.1142/s0129065794000220.

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This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, allowing a priori knowledge to be incorporated into their design. In problems where training data are scarce, such modular systems are likely to generalize significantly better than a monolithic connectionist system. In addition, modules are not restricted to be connectionist: hybrid systems, with e.g. Hidden Markov Models (HMMs), can be designed, combining the advantages of connectionist and non

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Nechanský, Tomáš, Tomáš Bořil, Alžběta Houzar, and Radek Skarnitzl. "The impact of mismatched recordings on an automatic-speaker-recognition system and human listeners." AUC PHILOLOGICA 2022, no. 1 (2023): 11–22. http://dx.doi.org/10.14712/24646830.2022.25.

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The so-called ‘mismatch’ is a factor which experts in the forensic voice comparison field encounter regularly. Therefore, we decided to explore to what extent the automatic-speaker-recognition system’s and the earwitness’ ability to identify speakers is influenced when recordings are acquired in different languages and at different times. 100 voices in a database of 300 recordings (100 speakers recorded in three mutually mismatched sessions) were compared with an automatic-speaker-recognition software VOCALISE based on i-vectors and x-vectors, and by 39 respondents in simulated voice parades.

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Indu D. "A Methodology for Speaker Diazaration System Based on LSTM and MFCC Coefficients." Journal of Electrical Systems 20, no. 6s (2024): 2938–45. http://dx.doi.org/10.52783/jes.3299.

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Research on Speaker Identification is always difficult. A speaker may be automatically identified using by comparing their voice sample with their previously recorded voice, the machine learning strategy has grown in favor in recent years. Convolutional neural networks (CNN) , deep neural networks (DNN) are some of the machine learning techniques that has employed recently. The article will discuss a successful speaker verification system based on the d-vector to construct a new approach based on speaker diarization. In particular, in this article, we use the concept of LSTM to cluster the spe

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El-Shafai, Walid, Marwa A. Elsayed, Mohsen A. Rashwan, et al. "Optical Ciphering Scheme for Cancellable Speaker Identification System." Computer Systems Science and Engineering 45, no. 1 (2023): 563–78. http://dx.doi.org/10.32604/csse.2023.024375.

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Jiang, Ming. "Fundamental frequency vector for a speaker identification system." International Journal of Speech Language and the Law 3, no. 1 (2013): 95–106. http://dx.doi.org/10.1558/ijsll.v3i1.95.

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Sekar, K. "Performance Analysis of Text-Independent Speaker Identification System." Procedia Engineering 38 (2012): 1735–44. http://dx.doi.org/10.1016/j.proeng.2012.06.211.

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Sekar, K. "Performance Analysis of Text-Independent Speaker Identification System." Procedia Engineering 38 (2012): 1925–34. http://dx.doi.org/10.1016/j.proeng.2012.06.235.

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Sekkate, Sara, Mohammed Khalil, and Abdellah Adib. "Speaker Identification for OFDM-Based Aeronautical Communication System." Circuits, Systems, and Signal Processing 38, no. 8 (2019): 3743–61. http://dx.doi.org/10.1007/s00034-019-01026-z.

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Hassan, Emad S., Badawi Neyazi, H. S. Seddeq, et al. "Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs." PLOS ONE 19, no. 2 (2024): e0294235. http://dx.doi.org/10.1371/journal.pone.0294235.

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This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb fi

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Al-Kaltakchi, Musab T. S., Haithem Abd Al-Raheem Taha, Mohanad Abd Shehab, and Mohamed A. M. Abdullah. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (2020): 782. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the M

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Fikri, Ahmad, and Amalia Zahra. "Speaker Identification in Multiple Languages: Regional, Indonesian, and English with Short Utterance." International Journal of Emerging Technology and Advanced Engineering 13, no. 9 (2023): 25–35. http://dx.doi.org/10.46338/ijetae0923_04.

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One of the authentication models that are currently often used is based on biometrics, such as eye retina, fingerprint, and speech recognition. Moreover, textindependent speaker identification is one of the domains of speech recognition that has been widely studied. Short speech duration in the speaker identification process is one of the challenges in the field of speaker recognition. Accuracy is a great issue when speech duration shorter, besides identification system has to be general enough to process various languages with different dialects which have their own characteristic based on tr

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