Line spectral frequencies modeling by a mixture of von Mises-Fisher distributions
2015 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 114, 219-224 p.Article in journal (Refereed) Published
Efficient quantization of the linear predictive coding (LPC) parameters plays a key role in parametric speech coding. The line spectral frequency (LSF) representation of the LPC parameters has found its applications in speech model quantization. In practical implementations of vector quantization (VQ), probability density function optimized VQ has been shown to be more efficient than the VQ based on training data. In this paper, we present the LSF parameters by a unit vector form, which has directional characteristics. The underlying distribution of this unit vector variable is modeled by a von Mises-Fisher mixture model (VMM). An optimal inter-component bit allocation strategy is proposed based on high rate theory and a distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms the recently introduced Dirichlet mixture model-based VQ and the conventional Gaussian mixture model-based VQ in terms of modeling performance and D-R relation.
Place, publisher, year, edition, pages
2015. Vol. 114, 219-224 p.
Speech coding, Line spectral frequencies, Vector quantization, Von Mises-Fisher distribution, Mixture modeling
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-169325DOI: 10.1016/j.sigpro.2015.02.015ISI: 000353853800021ScopusID: 2-s2.0-84961348106OAI: oai:DiVA.org:kth-169325DiVA: diva2:820822
QC 201506122015-06-122015-06-122015-06-12Bibliographically approved