Statistical Speech Model Description with VMF Mixture Model

TL;DR

This paper introduces a von Mises-Fisher mixture model for statistical speech modeling using unit vector LSF parameters, deriving an optimal bit allocation strategy and demonstrating superior performance over existing methods.

Contribution

It proposes a novel VMM-based approach for speech parameter modeling and develops a new distortion-rate relation for VQ, improving speech coding efficiency.

Findings

VVQ outperforms DVQ and GVQ in experiments

Optimal inter-component bit allocation enhances coding efficiency

Derived D-R relation for VMM-based VQ

Abstract

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). With the high rate theory, the optimal inter-component bit allocation strategy is proposed and the distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms our recently introduced DVQ and the conventional GVQ.