Journal ArticleDOI
Analysis and compensation of stressed and noisy speech with application to robust automatic recognition
TLDR
Combined speech enhancement stress compensation preprocessing is shown to be extremely effective in reducing and even eliminating effects caused by stress and noise for robust automatic recognition.About:
This article is published in Signal Processing.The article was published on 1988-01-01. It has received 102 citations till now. The article focuses on the topics: Speech enhancement & Speech production.read more
Citations
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Journal ArticleDOI
Speech recognition in noisy environments: a survey
TL;DR: The survey indicates that the essential points in noisy speech recognition consist of incorporating time and frequency correlations, giving more importance to high SNR portions of speech in decision making, exploiting task-specific a priori knowledge both of speech and of noise, using class-dependent processing, and including auditory models in speech processing.
Journal ArticleDOI
Nonlinear feature based classification of speech under stress
TL;DR: Three new features derived from the nonlinear Teager (1980) energy operator (TEO) are investigated for stress classification and it is shown that the TEO-CB-Auto-Env feature outperforms traditional pitch and mel-frequency cepstrum coefficients (MFCC) substantially.
Proceedings Article
Getting started with SUSAS: a speech under simulated and actual stress database.
TL;DR: The motivation for this paper is to familiarize the speech community with SUSAS, which was released April 1997 on CD-ROM through the NATO RSG and is intended to be employed in the study of how speech production and recognition varies during stressed conditions.
Journal ArticleDOI
A comparative study of traditional and newly proposed features for recognition of speech under stress
TL;DR: The results show that unlike fast Fourier transform's (FFT) immunity to noise, the linear prediction power spectrum is more immune than FFT to stress as well as to a combination of a noisy and stressful environment.
Journal ArticleDOI
Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition
TL;DR: It is suggested that recent studies based on a Source Generator Framework can provide a viable foundation in which to establish robust speech recognition techniques, and three novel approaches for signal enhancement and stress equalization are considered to address the issue of recognition under noisy stressful conditions.