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Automatic Speech Recognition Based on Neural Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Approaches for Out-of-Domain Adaptation to Improve Speaker Recognition Performance

Authors : Andrey Shulipa, Sergey Novoselov, Aleksandr Melnikov

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

In last years satisfactory performance of speaker recognition (SR) systems have been achieved in evaluations provided by NIST. It was possible due to using large datasets to train system parameters and accurate speaker variability modeling. In such a cases test and train conditions are similar and it ensures good performance for the evaluations. However in practical applications when training and testing conditions are different the problem of mismatching of the optimal SR system parameters occurs. It is the main problem in the deployment of the real application systems. It leads to reducing SR systems effectiveness. This paper investigates discriminative and generative approaches for the adaptation of the parameters of the speaker recognition systems and proposes effective solutions to improve their performance.

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previous chapter Advances in STC Russian Spontaneous Speech Recognition System
next chapter Assessment of the Relation Between Low-Frequency Features and Velum Opening by Using Real Articulatory Data
Literature
1.
2.
3.
Brümmer, N., de Villiers, E.: The bosaris toolkit: theory, algorithms and code for surviving the new dcf. arXiv preprint (2013). arXiv:​1304.​2865
4.
Dehak, N., Kenny, P.J., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio, Speech Lang. Process. 19(4), 788–798 (2011)CrossRef
5.
Doddington, G.R., Przybocki, M.A., Martin, A.F., Reynolds, D.A.: The NIST speaker recognition evaluation-overview, methodology, systems, results, perspective. Speech Commun. 31(2), 225–254 (2000)CrossRef
6.
Garcia-Romero, D., Espy-Wilson, C.Y.: Analysis of i-vector length normalization in speaker recognition systems. In: Interspeech, pp. 249–252 (2011)
7.
Garcia-Romero, D., McCree, A.: Supervised domain adaptation for i-vector based speaker recognition. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4047–4051. IEEE (2014)
8.
Garcia-Romero, D., McCree, A., Shum, S., Brummer, N., Vaquero, C.: Unsupervised domain adaptation for i-vector speaker recognition. In: Proceedings of Odyssey: The Speaker and Language Recognition Workshop (2014)
9.
Kenny, P.: Bayesian speaker verification with heavy-tailed priors. In: Odyssey, p. 14 (2010)
10.
Novoselov, S., Pekhovsky, T., Simonchik, K.: STC speaker recognition system for the NIST i-vector challenge. In: Odyssey: The Speaker and Language Recognition Workshop, pp. 231–240 (2014)
11.
Novoselov, S., Pekhovsky, T., Simonchik, K., Shulipa, A.: RBM-PLDA subsystem for the NIST i-vector challenge. System 8, 9 (2014)
12.
Pekhovsky, T., Novoselov, S., Sholohov, A., Kudashev, O.: On autoencoders in the i-vector space for speaker recognition
13.
Rohdin, J., Biswas, S., Shinoda, K.: Discriminative PLDA training with application-specific loss functions for speaker verification. In: Odyssey, The Speaker and Language Recognition Workshop (2014)
Metadata
Title
Approaches for Out-of-Domain Adaptation to Improve Speaker Recognition Performance
Authors
Andrey Shulipa
Sergey Novoselov
Aleksandr Melnikov
Copyright Year
2016
Book
Speech and Computer

Print ISBN: 978-3-319-43957-0

Electronic ISBN: 978-3-319-43958-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-43958-7_14

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