On the use of Total Variability and Probabilistic Linear Discriminant Analysis for Speaker Verification on Short Utterances
- Cite this paper as:
- Domínguez J.G., Zazo R., González-Rodríguez J. (2012) On the use of Total Variability and Probabilistic Linear Discriminant Analysis for Speaker Verification on Short Utterances. In: Torre Toledano D. et al. (eds) Advances in Speech and Language Technologies for Iberian Languages. Communications in Computer and Information Science, vol 328. Springer, Berlin, Heidelberg
This paper explores the use of state-of-the-art acoustic systems, namely Total Variability and Probabilistic Linear Discriminant Analysis for speaker verification on short utterances. While the recent advances in the field dealing with the session variability problem have proved to greatly outperform speaker verification systems on typical scenarios where a reasonable amount of speech is available, this performance rapidly degrades at the presence of limited data in both enrolment and verification stages. This paper studies the behaviour of TV and PLDA on those scenarios where a scarce amount of speech (~10s) is available to train and testing a speaker identity. The analysis has been carried out on the well defined and standard 10s-10s task belonging to the NIST Speaker Recognition Evaluation 2010 (NIST SRE10) and it explores the multiple parameters, which define TV and PLDA in order to give some insight about their relevance in this specific scenario.
Keywordsi-vectors Total variability PLDA short utterances
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