On von-Mises Fisher mixture model in Text-independent speaker identification
2013 (English)In: Proceedings of the 2013 INTERSPEECH, 2013, 2499-2503 p.Conference paper (Refereed)
This paper addresses text-independent speaker identification (SI) based on line spectral frequencies (LSFs). The LSFs are transformed to differential LSFs (MLSF) in order to exploit their boundary and ordering properties. We show that the square root of MLSF has interesting directional characteristics implying that their distribution can be modeled by a mixture of von-Mises Fisher (vMF) distributions. We analytically estimate the mixture model parameters in a fully Bayesian treatment by using variational inference. In the Bayesian inference, we can potentially determine the model complexity and avoid overfitting problem associated with conventional approaches based on the expectation maximization. The experimental results confirm the effectiveness of the proposed SI system.
Place, publisher, year, edition, pages
2013. 2499-2503 p.
, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, ISSN 2308-457X
Bayesian estimation, Line spectral frequencies, Mixture model, Text-independent speaker identification, Von-mises fisher distribution
Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-150987ScopusID: 2-s2.0-84906272590OAI: oai:DiVA.org:kth-150987DiVA: diva2:746343
14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013, 25 August 2013 through 29 August 2013, Lyon, France
QC 201409122014-09-122014-09-122014-09-12Bibliographically approved