Influence of Different Speech Representations and HMM Training Strategies on ASR Performance

Authors

  • H. Bořil
  • P. Fousek

DOI:

https://doi.org/10.14311/896

Keywords:

PLP, MFCC, Lombard effect, CLSD’05

Abstract

This work studies the influence of various speech signal representations and speaking styles on the performance of automatic speech recognition (ASR).  The efficiency of two approaches to hidden Markov model (HMM) training are compared.Common MFCC and PLP features were exposed to two sources of disturbance applied to the original wide-band speech: (i) stress (Lombard effect) and (ii) transfer channel distortion (simulated telephone line). Subsequently, the efficiencies of the two training strategies were evaluated. Finally, a study of the optimal number of training iterations is introduced. 

Downloads

Download data is not yet available.

Author Biographies

H. Bořil

P. Fousek

Downloads

Published

2006-01-06

How to Cite

Bořil, H., & Fousek, P. (2006). Influence of Different Speech Representations and HMM Training Strategies on ASR Performance. Acta Polytechnica, 46(6). https://doi.org/10.14311/896

Issue

Section

Articles