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2016 | OriginalPaper | Chapter

Influence of Noise and Voice Activity Detection on Speaker Verification

Author : Adam Dustor

Published in: Computer Networks

Publisher: Springer International Publishing

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Abstract

The scope of this paper is to check influence of voice activity detection VAD procedure and its accuracy on speaker verification error rates. It is shown that for speech of high quality, it is absolutely necessary to remove silence from the signal as the errors increase radically. It is better to remove more than less from the signal as the equal error rate EER is the worst for the original speech with silence. Additionally influence of white noise, which was added to speech utterances, was examined. Presented results show that in order to achieve highly reliable speaker verification system it must be insensitive to low quality of speech, since noise is the most important factor responsible for high error rates.

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previous chapter The Impact of the Degree of Self-Similarity on the NLREDwM Mechanism with Drop from Front Strategy
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Literature
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Dustor, A.: Voice verification based on nonlinear Ho-Kashyap classifier. In: International Conference on Computational Technologies in Electrical and Electronics Engineering SIBIRCON 2008, pp. 296–300. Novosibirsk (2008)
2.
Dustor, A., Szwarc, P.: Application of GMM models to spoken language recognition. In: Napieralski, A. (ed.) MIXDES 2009: Proceedings of the 16th International Conference Mixed Design of Integrated Circuits and Systems Lodz, Poland, pp. 603–606 (2009)
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Dustor, A.: Speaker verification based on fuzzy classifier. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 389–397. Springer, Heidelberg (2009)CrossRef
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Dustor, A., Szwarc, P.: Spoken language identification based on GMM models. In: Pulka, A., Golonek, T. (eds.) Inetrnational Conference on Signals and Electronic Systems (ICSES 2010): Conference Proceedings, Poland, Gliwice, pp. 105–108 (2010)
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Dustor, A., Kłosowski, P.: Biometric voice identification based on fuzzy kernel classifier. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2013. CCIS, vol. 370, pp. 456–465. Springer, Heidelberg (2013)CrossRef
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Kłosowski, P., Dustor, A.: Automatic speech segmentation for automatic speech translation. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2013. CCIS, vol. 370, pp. 466–475. Springer, Heidelberg (2013)CrossRef
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Dustor, A., Kłosowski, P., Izydorczyk, J.: Influence of feature dimensionality and model complexity on speaker verification performance. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 177–186. Springer, Heidelberg (2014)CrossRef
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Dustor, A., Klosowski, P., Izydorczyk, J.: Speaker recognition system with good generalization properties. In: 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco, pp. 206–210 (2014)
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Kłosowski, P., Dustor, A., Izydorczyk, J., Kotas, J., Ślimok, J.: Speech recognition based on open source speech processing software. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 308–317. Springer, Heidelberg (2014)CrossRef
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Kłosowski, P., Dustor, A., Izydorczyk, J.: Speaker verification performance evaluation based on open source speech processing software and TIMIT speech corpus. In: Gaj, P., Kwiecień, A., Stera, P. (eds.) CN 2015. CCIS, vol. 522, pp. 400–409. Springer, Heidelberg (2015)CrossRef
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Fazel, A., Chakrabartty, S.: An overview of statistical pattern recognition techniques for speaker verification. IEEE Circuits Syst. Mag. 11(2), 62–81 (2011)CrossRef
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Adamczyk, B., Adamczyk, K., Trawiński, K.: Zasób mowy ROBOT. Biuletyn Instytutu Automatyki i Robotyki WAT 12, 179–192 (2000)
Metadata
Title
Influence of Noise and Voice Activity Detection on Speaker Verification
Author
Adam Dustor
Copyright Year
2016
Book
Computer Networks

Print ISBN: 978-3-319-39206-6

Electronic ISBN: 978-3-319-39207-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39207-3_18

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