Publications – Details

 

Estimation of Missing LSF Parameters Using Gaussian Mixture Models

Authors:

Rainer Martin, Carsten Hoelper, and Ingo Wittke

Book Title:

Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Venue:

Salt Lake City, Utah, USA

Date:

May 2001

Pages:

729–732

Language:

English

Abstract

Speech transmission over packet networks has to cope with packet delays and packet losses. When a packet loss occurs the missing information must be estimated. We focus on restoring the spectral parameters of a speech coder. A novel approach to estimating missing line spectral frequency (LSF) parameters using Gaussian mixture models (GMM) is proposed. We present the estimation algorithm and study its performance when one or several LSF parameters are lost. We show that a GMM of a relatively low order is sufficient to achieve a substantial improvement in the parameter SNR. Therefore, the new estimation procedure requires much less memory than histogram based estimation methods.

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