Feature Extraction for a Speech Recognition System in Noisy Environment: A Study
Urmila N. Shrawankar, Vilas M. Thakare · 2010
Speech feature extraction has been a key focus in robust speech recognition research. Selecting proper features is the key of effective system performance. Robustness to additive noise remains a large unsolved problem in automatic speech recognition research today. One of the environmental changes that have a large impact on the performance of current ASR systems is background noise. There are several approaches that one can take to improve ASR systems robustness [7, 10] to changes in background noise. One of these approaches is to address the problem at the feature extraction stage of the system. That is, to use a speech feature extraction algorithm that produces features that are as invariant as possible to background noise changes, while simultaneously capturing the salient speech information. Many feature extraction algorithms have been proposed that are designed specifically to have a low sensitivity to background noise. In this paper we are presenting some feature extraction algorithm developed for noisy environment.