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Journal articles on the topic 'Text-independent speaker verification systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Rakhmanenko, I. A., A. A. Shelupanov, and E. Y. Kostyuchenko. "Automatic text-independent speaker verification using convolutional deep belief network." Computer Optics 44, no. 4 (2020): 596–605. http://dx.doi.org/10.18287/2412-6179-co-621.

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This paper is devoted to the use of the convolutional deep belief network as a speech feature extractor for automatic text-independent speaker verification. The paper describes the scope and problems of automatic speaker verification systems. Types of modern speaker verification systems and types of speech features used in speaker verification systems are considered. The structure and learning algorithm of convolutional deep belief networks is described. The use of speech features extracted from three layers of a trained convolution deep belief network is proposed. Experimental studies of the

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2

Shim, Hye-jin, Jee-weon Jung, Ju-ho Kim, and Ha-jin Yu. "Integrated Replay Spoofing-Aware Text-Independent Speaker Verification." Applied Sciences 10, no. 18 (2020): 6292. http://dx.doi.org/10.3390/app10186292.

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A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypoth

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3

Auckenthaler, Roland, Michael Carey, and Harvey Lloyd-Thomas. "Score Normalization for Text-Independent Speaker Verification Systems." Digital Signal Processing 10, no. 1-3 (2000): 42–54. http://dx.doi.org/10.1006/dspr.1999.0360.

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4

Das, Rohan Kumar, and S. R. Mahadeva Prasanna. "Investigating Text-Independent Speaker Verification Systems Under Varied Data Conditions." Circuits, Systems, and Signal Processing 38, no. 8 (2019): 3778–801. http://dx.doi.org/10.1007/s00034-019-01028-x.

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5

Shahin, Ismail. "Employing Emotion Cues to Verify Speakers in Emotional Talking Environments." Journal of Intelligent Systems 25, no. 1 (2016): 3–17. http://dx.doi.org/10.1515/jisys-2014-0118.

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AbstractUsually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people’s talking tone include happiness, anger, and sadness. Such emotions are directly affected by the patient’s health status. In neutral talking environments, speakers can be easily verified; however, in emotional talking environments, speakers cannot be easily verified as in neutral talking ones. Consequently, speaker verification systems do not perform well in emotional talking environments as they do in neutral talk

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6

ZHAO, Jian, Yuan DONG, Xian-yu ZHAO, Hao YANG, and Hai-la WANG. "Cross similarity measurement for speaker adaptive test normalization in text-independent speaker verification." Journal of China Universities of Posts and Telecommunications 15, no. 2 (2008): 130–34. http://dx.doi.org/10.1016/s1005-8885(08)60097-7.

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7

Mariéthoz, Johnny, and Samy Bengio. "A kernel trick for sequences applied to text-independent speaker verification systems." Pattern Recognition 40, no. 8 (2007): 2315–24. http://dx.doi.org/10.1016/j.patcog.2007.01.011.

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8

Mao, Hongwei, Yan Shi, Yue Liu, Linqiang Wei, Yijie Li, and Yanhua Long. "Short-time speaker verification with different speaking style utterances." PLOS ONE 15, no. 11 (2020): e0241809. http://dx.doi.org/10.1371/journal.pone.0241809.

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In recent years, great progress has been made in the technical aspects of automatic speaker verification (ASV). However, the promotion of ASV technology is still a very challenging issue, because most technologies are still very sensitive to new, unknown and spoofing conditions. Most previous studies focused on extracting target speaker information from natural speech. This paper aims to design a new ASV corpus with multi-speaking styles and investigate the ASV robustness to these different speaking styles. We first release this corpus in the Zenodo website for public research, in which each s

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9

Zhang, Y., X. Zhu, and D. Zhang. "Correction to A novel text-independent speaker verification method based on the global speaker model." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 6 (2000): 883. http://dx.doi.org/10.1109/tsmca.2000.895929.

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10

Shi, Yan, Juanjuan Zhou, Yanhua Long, Yijie Li, and Hongwei Mao. "Addressing Text-Dependent Speaker Verification Using Singing Speech." Applied Sciences 9, no. 13 (2019): 2636. http://dx.doi.org/10.3390/app9132636.

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The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compa

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11

Li, Ming, Lun Liu, Weicheng Cai, and Wenbo Liu. "Generalized I-vector Representation with Phonetic Tokenizations and Tandem Features for both Text Independent and Text Dependent Speaker Verification." Journal of Signal Processing Systems 82, no. 2 (2015): 207–15. http://dx.doi.org/10.1007/s11265-015-1019-z.

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12

ZERGAT, KAWTHAR YASMINE, and ABDERRAHMANE AMROUCHE. "SVM AGAINST GMM/SVM FOR DIALECT INFLUENCE ON AUTOMATIC SPEAKER RECOGNITION TASK." International Journal of Computational Intelligence and Applications 13, no. 02 (2014): 1450012. http://dx.doi.org/10.1142/s1469026814500126.

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A big deal for current research on automatic speaker recognition is the effectiveness of the speaker modeling techniques for the talkers, because they have their own speaking style, depending on their specific accents and dialects. This paper investigates on the influence of the dialect and the size of database on the text independent speaker verification task using the SVM and the hybrid GMM/SVM speaker modeling. The Principal Component Analysis (PCA) technique is used in the front-end part of the speaker recognition system, in order to extract the most representative features. Experimental r

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13

Wang, Wei, Jiqing Han, Tieran Zheng, Guibin Zheng, and Xingyu Zhou. "Speaker Verification via Modeling Kurtosis Using Sparse Coding." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (2016): 1659008. http://dx.doi.org/10.1142/s0218001416590084.

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This paper proposes a new model for speaker verification by employing kurtosis statistical method based on sparse coding of human auditory system. Since only a small number of neurons in primary auditory cortex are activated in encoding acoustic stimuli and sparse independent events are used to represent the characteristics of the neurons. Each individual dictionary is learned from individual speaker samples where dictionary atoms correspond to the cortex neurons. The neuron responses possess statistical properties of acoustic signals in auditory cortex so that the activation distribution of i

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14

Nemati, Shahla, Reza Boostani, Mohammad Davarpanah Jazi, Mehdi Hosseinzadeh Aghdam, and Ehsan Basiri. "Corrigendum to “Text-independent speaker verification using ant colony optimization-based selected features” [Expert Systems with Applications 38 (1) (2011) 620–630]." Expert Systems with Applications 38, no. 4 (2011): 4658. http://dx.doi.org/10.1016/j.eswa.2010.11.010.

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15

Hussien, Emad Ahmed, Mohannad Abid Shehab Ahmed, and Haithem Abd Al-Raheem Taha. "Speech Recognition using Wavelets and Improved SVM." Wasit Journal of Engineering Sciences 1, no. 2 (2013): 55–78. http://dx.doi.org/10.31185/ejuow.vol1.iss2.13.

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Speaker recognition (identification/verification) is the computing task of validating a user’s claimed identity using speaker specific information included in speech waves: that is, it enables access control of various services by voice. Discrete Wavelet Transform (DWT) based systems for speaker recognition have shown robust results for several years and are widely used in speaker recognition applications. This paper is based on text independent speaker recognition system that makes use of Discrete Wavelet Transform (DWT) as a feature extraction and kernel Support Vector Machine (SVM) approach

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16

Abushariah, Mohammad A. M., and Assal A. M. Alqudah. "Automatic Identity Recognition Using Speech Biometric." European Scientific Journal, ESJ 12, no. 12 (2016): 43. http://dx.doi.org/10.19044/esj.2016.v12n12p43.

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Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recogniz

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17

Lutsenko, K., and K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON." Theory and Practice of Forensic Science and Criminalistics 19, no. 1 (2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the correspon

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18

Rudramurthy, M. S., Nilabh Kumar Pathak, V. Kamakshi Prasad, and R. Kumaraswamy. "Speaker Identification Using Empirical Mode Decomposition-Based Voice Activity Detection Algorithm under Realistic Conditions." Journal of Intelligent Systems 23, no. 4 (2014): 405–21. http://dx.doi.org/10.1515/jisys-2013-0089.

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AbstractSpeaker recognition (SR) under mismatched conditions is a challenging task. Speech signal is nonlinear and nonstationary, and therefore, difficult to analyze under realistic conditions. Also, in real conditions, the nature of the noise present in speech data is not known a priori. In such cases, the performance of speaker identification (SI) or speaker verification (SV) degrades considerably under realistic conditions. Any SR system uses a voice activity detector (VAD) as the front-end subsystem of the whole system. The performance of most VADs deteriorates at the front end of the SR t

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19

Zilca, R. D. "Text-independent speaker verification using covariance modeling." IEEE Signal Processing Letters 8, no. 4 (2001): 97–99. http://dx.doi.org/10.1109/97.911465.

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20

Day, Peter, and Asoke K. Nandi. "Genetic Programming for Robust Text Independent Speaker Verification." International Journal of Signs and Semiotic Systems 2, no. 2 (2012): 1–22. http://dx.doi.org/10.4018/ijsss.2012070101.

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Robust Automatic Speaker Verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, Genetic Programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited for this task. This article introduces a Genetic Programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT

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21

Day, Peter, and Asoke K. Nandi. "Robust Text-Independent Speaker Verification Using Genetic Programming." IEEE Transactions on Audio, Speech and Language Processing 15, no. 1 (2007): 285–95. http://dx.doi.org/10.1109/tasl.2006.876765.

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22

Bing Xiang. "Text-independent speaker verification with dynamic trajectory model." IEEE Signal Processing Letters 10, no. 5 (2003): 141–43. http://dx.doi.org/10.1109/lsp.2003.810913.

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23

Pinheiro, Hector N. B., Tsang Ing Ren, André G. Adami, and George D. C. Cavalcanti. "Variational DNN embeddings for text-independent speaker verification." Pattern Recognition Letters 148 (August 2021): 100–106. http://dx.doi.org/10.1016/j.patrec.2021.05.003.

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24

GUOJIE, LI, P. SARATCHANDRAN, and N. SUNDARARAJAN. "TEXT-INDEPENDENT SPEAKER VERIFICATION USING MINIMAL RESOURCE ALLOCATION NETWORKS." International Journal of Neural Systems 14, no. 06 (2004): 347–54. http://dx.doi.org/10.1142/s0129065704002108.

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This paper presents a text-independent speaker verification system based on an online Radial Basis Function (RBF) network referred to as Minimal Resource Allocation Network (MRAN). MRAN is a sequential learning RBF, in which hidden neurons are added or removed as training progresses. LP-derived cepstral coefficients are used as feature vectors during training and verification phases. The performance of MRAN is compared with other well-known RBF and Elliptical Basis Function (EBF) based speaker verification methods in terms of error rates and computational complexity on a series of speaker veri

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25

Moattar, Mohammad Hossein. "Text-Independent Speaker Verification Using Variational Gaussian Mixture Model." ETRI Journal 33, no. 6 (2011): 914–23. http://dx.doi.org/10.4218/etrij.11.0110.0684.

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26

Lee, Heungkyu, and Hanseok Ko. "Competing models-based text-prompted speaker independent verification algorithm." Speech Communication 48, no. 1 (2006): 28–44. http://dx.doi.org/10.1016/j.specom.2005.05.014.

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27

Xu, Jiwei, Xinggang Wang, Bin Feng, and Wenyu Liu. "Deep multi-metric learning for text-independent speaker verification." Neurocomputing 410 (October 2020): 394–400. http://dx.doi.org/10.1016/j.neucom.2020.06.045.

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28

Lund, Michael A., and C. C. Lee. "A robust sequential test for text‐independent speaker verification." Journal of the Acoustical Society of America 99, no. 1 (1996): 609–21. http://dx.doi.org/10.1121/1.414516.

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29

Chen, Liping, Kong Aik Lee, Bin Ma, Wu Guo, Haizhou Li, and Li-Rong Dai. "Exploration of Local Variability in Text-Independent Speaker Verification." Journal of Signal Processing Systems 82, no. 2 (2015): 217–28. http://dx.doi.org/10.1007/s11265-015-0997-1.

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30

Yiying Zhang, D. Zhang, and Xiaoyan Zhu. "A novel text-independent speaker verification method based on the global speaker model." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 5 (2000): 598–602. http://dx.doi.org/10.1109/3468.867867.

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31

Zabihzadeh, Davood, and Mohammad H. Moattar. "Manifold learning based speaker dependent dimension reduction for robust text independent speaker verification." International Journal of Speech Technology 17, no. 3 (2014): 271–80. http://dx.doi.org/10.1007/s10772-014-9228-6.

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32

ZHAO, Jing, Wei-guo GONG, and Li-ping YANG. "Mandarin-Sichuan dialect bilingual text-independent speaker verification using GMM." Journal of Computer Applications 28, no. 3 (2008): 792–94. http://dx.doi.org/10.3724/sp.j.1087.2008.00792.

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33

Xu, Longting, Bo Ren, Guanglin Zhang, and Jichen Yang. "Linear transformation on x‐vector for text‐independent speaker verification." Electronics Letters 55, no. 15 (2019): 864–66. http://dx.doi.org/10.1049/el.2019.1264.

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34

Ganchev, Todor, Ilyas Potamitis, Nikos Fakotakis, and George Kokkinakis. "Text-Independent Speaker Verification for Real Fast-Varying Noisy Environments." International Journal of Speech Technology 7, no. 4 (2004): 281–92. http://dx.doi.org/10.1023/b:ijst.0000037072.36778.9e.

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35

Tishby, Naftali. "Text‐independent speaker verification using linear predictive hidden Markov models." Journal of the Acoustical Society of America 81, S1 (1987): S94. http://dx.doi.org/10.1121/1.2024482.

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36

MUKAI, Y., H. NODA, M. NIIMI, and T. OSANAI. "Text-Independent Speaker Verification Using Artificially Generated GMMs for Cohorts." IEICE Transactions on Information and Systems E91-D, no. 10 (2008): 2536–39. http://dx.doi.org/10.1093/ietisy/e91-d.10.2536.

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37

Thévenaz, Philippe, and Heinz Hügli. "Usefulness of the LPC-residue in text-independent speaker verification." Speech Communication 17, no. 1-2 (1995): 145–57. http://dx.doi.org/10.1016/0167-6393(95)00010-l.

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38

Singh, Renu, Arvind Singh, and Utpal Bhattacharjee. "A Review on Text-Independent Speaker Verification Techniques in Realistic World." Oriental journal of computer science and technology 9, no. 1 (2016): 36–40. http://dx.doi.org/10.13005/ojcst/901.07.

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This paper presents a reviewof various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

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39

Zhang, Chunlei, Kazuhito Koishida, and John H. L. Hansen. "Text-Independent Speaker Verification Based on Triplet Convolutional Neural Network Embeddings." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 9 (2018): 1633–44. http://dx.doi.org/10.1109/taslp.2018.2831456.

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40

Wang, Shuai, Zili Huang, Yanmin Qian, and Kai Yu. "Discriminative Neural Embedding Learning for Short-Duration Text-Independent Speaker Verification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 11 (2019): 1686–96. http://dx.doi.org/10.1109/taslp.2019.2928128.

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41

DONG, Yuan, Liang LU, Xian-Yu ZHAO, and Jian ZHAO. "Studies on Model Distance Normalization Approach in Text-independent Speaker Verification." Acta Automatica Sinica 35, no. 5 (2009): 556–60. http://dx.doi.org/10.1016/s1874-1029(08)60086-5.

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42

Zilca, R. D. "Text-independent speaker verification using utterance level scoring and covariance modeling." IEEE Transactions on Speech and Audio Processing 10, no. 6 (2002): 363–70. http://dx.doi.org/10.1109/tsa.2002.803419.

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43

Gupta, Sunil K., and Michael Savic. "Text-independent speaker verification based on broad phonetic segmentation of speech." Digital Signal Processing 2, no. 2 (1992): 69–79. http://dx.doi.org/10.1016/1051-2004(92)90027-v.

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44

Liu, Chi-shi, and Hsiao-Chuan Wang. "A stochastic fixed length segment model for text independent speaker verification." Signal Processing 45, no. 2 (1995): 183–91. http://dx.doi.org/10.1016/0165-1684(95)00050-n.

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45

Nemati, Shahla, and Mohammad Ehsan Basiri. "Text-independent speaker verification using ant colony optimization-based selected features." Expert Systems with Applications 38, no. 1 (2011): 620–30. http://dx.doi.org/10.1016/j.eswa.2010.07.011.

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46

NURATCH, Santi, Panuthat BOONPRAMUK, and Chai WUTIWIWATCHAI. "A Time-Varying Adaptive IIR Filter for Robust Text-Independent Speaker Verification." IEICE Transactions on Information and Systems E96.D, no. 3 (2013): 699–707. http://dx.doi.org/10.1587/transinf.e96.d.699.

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47

Zeinali, Hossein, Hossein Sameti, and Lukas Burget. "HMM-Based Phrase-Independent i-Vector Extractor for Text-Dependent Speaker Verification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 7 (2017): 1421–35. http://dx.doi.org/10.1109/taslp.2017.2694708.

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48

Hansen, John H. L., Mahesh Kumar Nandwana, and Navid Shokouhi. "Analysis of human scream and its impact on text-independent speaker verification." Journal of the Acoustical Society of America 141, no. 4 (2017): 2957–67. http://dx.doi.org/10.1121/1.4979337.

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49

Hema, Palivela. "Performance Evaluation of Text-Independent Speaker Identification and Verification Using MFCC and GMM." IOSR Journal of Engineering 02, no. 08 (2012): 18–22. http://dx.doi.org/10.9790/3021-02861822.

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50

Ganchev, Todor D., Dimitris K. Tasoulis, Michael N. Vrahatis, and Nikos D. Fakotakis. "Generalized locally recurrent probabilistic neural networks with application to text-independent speaker verification." Neurocomputing 70, no. 7-9 (2007): 1424–38. http://dx.doi.org/10.1016/j.neucom.2006.05.012.

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