Abstract
Speaker recognition systems proposed in the literature are conditioned by many factors such as the features used, the type of data to be processed, data duration and the approach to be used. This work presents a novel speaker recognition application using a new dimensional cepstral feature vector for building Gaussian Mixture Models (GMM) for speaker identification and verification systems. Recognition results obtained with the proposed system are compared to the state of the art systems requiring high dimensional feature vectors. We experimentally evaluate this work with speakers taken from TIMIT database. The new system gave substantial improvements even when trained with limited data, which explain the effectiveness of the new feature vectors with this approach which can be comparable to the use additional algorithms or approaches.
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References
Dehak, N., Karam, Z., Reynolds, D., Dehak, R., Campbell, W., Glass, J.: A channel-blind system for speaker verification. In: Proceedings of ICASSP, Prague, Czech Republic, pp. 4536–4539, May 2011
Dehak, N., Kenny, P., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19(4), 788–798 (2011)
Togneri, R., Pullella, R.: An overview of speaker identification: accuracy and robustness issues. IEEE Circuits Syst. Mag. 11(2), 23–61 (2011). ISSN: 1531-636X
Liu, Z., Wu, Z., Li, T., Li, J., Shen, C.: GMM and CNN hybrid method for short utterance speaker recognition. IEEE Trans. Industr. Inf. 14(7), 3244–3252 (2018)
Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: From features to supervectors. Speech Commun. 52(1), 12–40 (2010)
Bimbot, F., et al.: A tutorial on text-independent speaker verification. EURASIP J. Adv. Signal Process. 2004(4), 101962 (2004)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digit. Signal Process. 10(1–3), 19–41 (2000)
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Commun. 17(1–2), 91–108 (1995)
Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: Hidden Markov model toolkit (htk) version 3.4 user’s guide (2002)
Campbell, W., Sturim, D., Reynolds, D.: Support vector machines using GMM supervectors for speaker verification. IEEE Signal Process. Lett. 13(5), 308–311 (2006)
Liu, Z., Wu, Z., Li, T., Li, J., Shen, C.: GMM and CNN hybrid method for short utterance speaker recognition. IEEE Trans. Industr. Inf. 14, 3244–3252 (2018)
Jokinen, E., Saeidi, R., Kinnunen, T., Alku, P.: Vocal effort compensation for MFCC feature extraction in a shouted versus normal speaker recognition task. Comput. Speech Lang. 53, 1–11 (2019)
Chettri, B., Sturm, B.L.: A deeper look at Gaussian mixture model based anti-spoofing systems. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5159–5163. IEEE, April 2018
Venkatesan, R., Ganesh, A.B.: Binaural classification-based speech segregation and robust speaker recognition system. Circuits Syst. Signal Process. 37(8), 3383–3411 (2018)
Hasan, T., Saeidi, R., Hansen, J. H., Van Leeuwen, D.A.: Duration mismatch compensation for i-vector based speaker recognition systems. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7663–7667. IEEE, May 2013
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Chakroun, R., Frikha, M. (2019). Improved Text-Independent Speaker Identification and Verification with Gaussian Mixture Models. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_1
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DOI: https://doi.org/10.1007/978-3-030-29563-9_1
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