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Journal articles on the topic 'Robust speaker identification'

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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2

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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3

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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4

Jia-Ching Wang, Chung-Hsien Yang, Jhing-Fa Wang, and Hsiao-Ping Lee. "Robust Speaker Identification and Verification." IEEE Computational Intelligence Magazine 2, no. 2 (2007): 52–59. http://dx.doi.org/10.1109/mci.2007.353420.

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5

Zhao, Xiaojia, Yang Shao, and DeLiang Wang. "CASA-Based Robust Speaker Identification." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 5 (2012): 1608–16. http://dx.doi.org/10.1109/tasl.2012.2186803.

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6

Bose, Smarajit, Amita Pal, Anish Mukherjee, and Debasmita Das. "Robust Speaker Identification Using Fusion of Features and Classifiers." International Journal of Machine Learning and Computing 7, no. 5 (2017): 133–38. http://dx.doi.org/10.18178/ijmlc.2017.7.5.635.

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7

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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8

Fredj, Ines Ben, Youssef Zouhir, and Kaïs Ouni. "Fusion features for robust speaker identification." International Journal of Signal and Imaging Systems Engineering 11, no. 2 (2018): 65. http://dx.doi.org/10.1504/ijsise.2018.091881.

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9

Fredj, Ines Ben, Youssef Zouhir, and Kaïs Ouni. "Fusion features for robust speaker identification." International Journal of Signal and Imaging Systems Engineering 11, no. 2 (2018): 65. http://dx.doi.org/10.1504/ijsise.2018.10013027.

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10

Milošević, M., Ž. Nedeljković, U. Glavitsch, and Ž. Đurović. "Speaker Modeling Using Emotional Speech for More Robust Speaker Identification." Journal of Communications Technology and Electronics 64, no. 11 (2019): 1256–65. http://dx.doi.org/10.1134/s1064226919110184.

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11

Reynolds, D. A., and R. C. Rose. "Robust text-independent speaker identification using Gaussian mixture speaker models." IEEE Transactions on Speech and Audio Processing 3, no. 1 (1995): 72–83. http://dx.doi.org/10.1109/89.365379.

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12

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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13

Ghalamiosgouei, Sina, and Masoud Geravanchizadeh. "Robust Speaker Identification Based on Binaural Masks." Speech Communication 132 (September 2021): 1–9. http://dx.doi.org/10.1016/j.specom.2021.05.007.

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14

Latha. "Robust Speaker Identification Incorporating High Frequency Features." Procedia Computer Science 89 (2016): 804–11. http://dx.doi.org/10.1016/j.procs.2016.06.064.

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15

Mashao, Daniel J., and Marshalleno Skosan. "Combining classifier decisions for robust speaker identification." Pattern Recognition 39, no. 1 (2006): 147–55. http://dx.doi.org/10.1016/j.patcog.2005.08.004.

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16

Faragallah, Osama S. "Robust noise MKMFCC–SVM automatic speaker identification." International Journal of Speech Technology 21, no. 2 (2018): 185–92. http://dx.doi.org/10.1007/s10772-018-9494-9.

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17

Liao, Yuan-Fu, Zi-He Chen, and Yau-Tarng Juang. "Latent Prosody Analysis for Robust Speaker Identification." IEEE Transactions on Audio, Speech and Language Processing 15, no. 6 (2007): 1870–83. http://dx.doi.org/10.1109/tasl.2007.896660.

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18

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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19

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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20

V., Sharada, and Mahesh S. "Performance of Complementary Features for Robust Speaker Identification." International Journal of Computer Applications 123, no. 9 (2015): 21–27. http://dx.doi.org/10.5120/ijca2015905617.

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21

Chen, C. C. T., C. T. Chen, and P. W. Cheng. "Hybrid KLT∕GMM approach for robust speaker identification." Electronics Letters 39, no. 21 (2003): 1552. http://dx.doi.org/10.1049/el:20030925.

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22

Wu, Zunjing, and Zhigang Cao. "Improved MFCC-based feature for robust speaker identification." Tsinghua Science and Technology 10, no. 2 (2005): 158–61. http://dx.doi.org/10.1016/s1007-0214(05)70048-1.

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23

El Ayadi, Moataz, Abdel-Karim S.O. Hassan, Ahmed Abdel-Naby, and Omar A. Elgendy. "Text-independent speaker identification using robust statistics estimation." Speech Communication 92 (September 2017): 52–63. http://dx.doi.org/10.1016/j.specom.2017.05.005.

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24

Reynolds, D. A. "Experimental evaluation of features for robust speaker identification." IEEE Transactions on Speech and Audio Processing 2, no. 4 (1994): 639–43. http://dx.doi.org/10.1109/89.326623.

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25

Murthy, H. A., F. Beaufays, L. P. Heck, and M. Weintraub. "Robust text-independent speaker identification over telephone channels." IEEE Transactions on Speech and Audio Processing 7, no. 5 (1999): 554–68. http://dx.doi.org/10.1109/89.784108.

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26

Zhao, Xiaojia, Yuxuan Wang, and DeLiang Wang. "Robust Speaker Identification in Noisy and Reverberant Conditions." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 4 (2014): 836–45. http://dx.doi.org/10.1109/taslp.2014.2308398.

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27

Li, Zuoqiang, and Yong Gao. "Acoustic feature extraction method for robust speaker identification." Multimedia Tools and Applications 75, no. 12 (2015): 7391–406. http://dx.doi.org/10.1007/s11042-015-2660-z.

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28

Nicolson, Aaron, Jack Hanson, James Lyons, and Kuldip Paliwal. "Spectral Subband Centroids for Robust Speaker Identification Using Marginalization-based Missing Feature Theory." International Journal of Signal Processing Systems 6, no. 1 (2018): 12–16. http://dx.doi.org/10.18178/ijsps.6.1.12-16.

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29

Koteswara Rao, P. Rama, Sunitha Ravi, and Thotakura Haritha. "Purging of silence for robust speaker identification in colossal database." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3084. http://dx.doi.org/10.11591/ijece.v11i4.pp3084-3092.

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The aim of this work is to develop an effective speaker recognition system under noisy environments for large data sets. The important phases involved in typical identification systems are feature extraction, training and testing. During the feature extraction phase, the speaker-specific information is processed based on the characteristics of the voice signal. Effective methods have been proposed for the silence removal in order to achieve accurate recognition under noisy environments in this work. Pitch and Pitch-strength parameters are extracted as distinct features from the input speech sp

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30

P., Rama Koteswara Rao, Ravi Sunitha, and Haritha Thotakura. "Purging of silence for robust speaker identification in colossal database." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3084–92. https://doi.org/10.11591/ijece.v11i4.pp3084-3092.

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The aim of this work is to develop an effective speaker recognition system under noisy environments for large data sets. The important phases involved in typical identification systems are feature extraction, training and testing. During the feature extraction phase, the speaker-specific information is processed based on the characteristics of the voice signal. Effective methods have been proposed for the silence removal in order to achieve accurate recognition under noisy environments in this work. Pitch and Pitch-strength parameters are extracted as distinct features from the input speech sp

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31

Shih, Po-Yi, Po-Chuan Lin, Jhing-Fa Wang, and Yuan-Ning Lin. "Robust several-speaker speech recognition with highly dependable online speaker adaptation and identification." Journal of Network and Computer Applications 34, no. 5 (2011): 1459–67. http://dx.doi.org/10.1016/j.jnca.2010.08.007.

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32

Alhanjouri, Mohammed, Mohammed A. H. Lubbad, and Mahmoud Z. Alkurdi. "Robust Speaker Identification using Denoised Wave Atom and GMM." International Journal of Computer Applications 67, no. 5 (2013): 17–23. http://dx.doi.org/10.5120/11391-6687.

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33

Upadhyay, Shrikant, and Sudhir Kumar Sharma. "Robust Speaker Identification and Verification in Adverse Acoustic Condition." INROADS- An International Journal of Jaipur National University 8, no. 1and2 (2019): 14. http://dx.doi.org/10.5958/2277-4912.2019.00002.x.

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34

Islam, Md Atiqul, Ying Xu, Travis Monk, Saeed Afshar, and André van Schaik. "Noise-robust text-dependent speaker identification using cochlear models." Journal of the Acoustical Society of America 151, no. 1 (2022): 500–516. http://dx.doi.org/10.1121/10.0009314.

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35

Islam, Md Rabiul, and Md Fayzur Rahman. "Noise Robust Speaker Identification using PCA based Genetic Algorithm." International Journal of Computer Applications 4, no. 12 (2010): 27–31. http://dx.doi.org/10.5120/875-1238.

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36

Falk, T. H., and Wai-Yip Chan. "Modulation Spectral Features for Robust Far-Field Speaker Identification." IEEE Transactions on Audio, Speech, and Language Processing 18, no. 1 (2010): 90–100. http://dx.doi.org/10.1109/tasl.2009.2023679.

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37

Saleem, Nasir, and Tayyaba Gul Tareen. "Spectral Restoration Based Speech Enhancement for Robust Speaker Identification." International Journal of Interactive Multimedia and Artificial Intelligence 5, no. 1 (2018): 34. http://dx.doi.org/10.9781/ijimai.2018.01.002.

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38

Deshpande, Mangesh S., and Raghunath S. Holambe. "Robust speaker identification in the presence of car noise." International Journal of Biometrics 3, no. 3 (2011): 189. http://dx.doi.org/10.1504/ijbm.2011.040815.

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39

Prasad, Swati, Zheng-Hua Tan, and Ramjee Prasad. "Frame Selection for Robust Speaker Identification: A Hybrid Approach." Wireless Personal Communications 97, no. 1 (2017): 933–50. http://dx.doi.org/10.1007/s11277-017-4544-1.

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40

Zarin, Syed Shahab, Ehzaz Mustafa, Sardar Khaliq uz Zaman, Abdallah Namoun, and Meshari Huwaytim Alanazi. "An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments." Applied Sciences 14, no. 22 (2024): 10426. http://dx.doi.org/10.3390/app142210426.

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Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system’s ability to identify speakers even in n

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41

Nematollahi, Mohammad Ali, Hamurabi Gamboa-Rosales, Mohammad Ali Akhaee, and S. A. R. Al-Haddad. "Robust Digital Speech Watermarking For Online Speaker Recognition." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/372398.

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A robust and blind digital speech watermarking technique has been proposed for online speaker recognition systems based on Discrete Wavelet Packet Transform (DWPT) and multiplication to embed the watermark in the amplitudes of the wavelet’s subbands. In order to minimize the degradation effect of the watermark, these subbands are selected where less speaker-specific information was available (500 Hz–3500 Hz and 6000 Hz–7000 Hz). Experimental results on Texas Instruments Massachusetts Institute of Technology (TIMIT), Massachusetts Institute of Technology (MIT), and Mobile Biometry (MOBIO) show

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42

TARIQUZZAMAN, Md, Jin Young KIM, Seung You NA, Hyoung-Gook KIM, and Dongsoo HAR. "A Visual Signal Reliability for Robust Audio-Visual Speaker Identification." IEICE Transactions on Information and Systems E94-D, no. 10 (2011): 2052–55. http://dx.doi.org/10.1587/transinf.e94.d.2052.

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43

XU, Li-Min. "Research on Robust Speaker Identification Based on Adaptive Histogram Equalization." Acta Automatica Sinica 34, no. 7 (2009): 752–59. http://dx.doi.org/10.3724/sp.j.1004.2008.00752.

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44

Chi, Tai-Shih, Ting-Han Lin, and Chung-Chien Hsu. "Spectro-temporal modulation energy based mask for robust speaker identification." Journal of the Acoustical Society of America 131, no. 5 (2012): EL368—EL374. http://dx.doi.org/10.1121/1.3697534.

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45

Missaoui, Ibrahim, and Zied Lachiri. "Stationary wavelet Filtering Cepstral coefficients (SWFCC) for robust speaker identification." Applied Acoustics 231 (March 2025): 110435. https://doi.org/10.1016/j.apacoust.2024.110435.

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46

Kwon, Soonil, and Shrikanth Narayanan. "Robust speaker identification based on selective use of feature vectors." Pattern Recognition Letters 28, no. 1 (2007): 85–89. http://dx.doi.org/10.1016/j.patrec.2006.06.009.

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47

Fukumori, Takahiro, Masanori Morise, Takanobu Nishiura, and Yoichi Yamashita. "An identification of speaker-dependence in reverberant-robust speech recognition." Journal of the Acoustical Society of America 131, no. 4 (2012): 3482. http://dx.doi.org/10.1121/1.4709138.

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48

May, Tobias. "Influence of binary mask estimation errors on robust speaker identification." Speech Communication 87 (March 2017): 40–48. http://dx.doi.org/10.1016/j.specom.2016.12.002.

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49

Zhang, Linghua, Baoyu Zheng, and Zhen Yang. "Statistical feature of pitch frequency distributions for robust speaker identification." Journal of Electronics (China) 22, no. 4 (2005): 437–42. http://dx.doi.org/10.1007/bf02687916.

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50

Sadjadi, Seyed Omid, and John H. L. Hansen. "Blind Spectral Weighting for Robust Speaker Identification under Reverberation Mismatch." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 5 (2014): 937–45. http://dx.doi.org/10.1109/taslp.2014.2311329.

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