A modulation feature set for robust Automatic Speech Recognition in additive noise and reverberation

Xiaoyu Liu; Roozbeh Sadeghian; Stephen A. Zahorian

ICASSP 2017accepted0 citations

A modulation feature set for robust Automatic Speech Recognition in additive noise and reverberation

Xiaoyu Liu, Roozbeh Sadeghian, Stephen A. Zahorian

Abstract

In this paper, a feature set referred to as Discrete Cosine Series (DCS) is proposed for noise robust Automatic Speech Recognition (ASR). Unlike many other robust algorithms which use various forms of “long term” processing, DCS uses a small frame spacing to facilitate separating speech from noise and also for other benefits. Spectral and temporal modulations are performed separately using only a small number of modulation filters. ASR experiments show the effectiveness of individual components of the DCS algorithm. The DCS features yield higher accuracy ASR for both additive noise and reverberation, as compared to several other advanced robust algorithms.

BibTeX

@inproceedings{icassp2017_amodulationfeatu,
  title = {A modulation feature set for robust Automatic Speech Recognition in additive noise and reverberation},
  author = {Xiaoyu Liu and Roozbeh Sadeghian and Stephen A. Zahorian},
  booktitle = {ICASSP 2017},
  year = {2017}
}