Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition

Abstract

It is well known that the introduction of acoustic background distortion and the variability resulting from environmentally induced stress causes speech recognition algorithms to fail. In this paper, several causes for recognition performance degradation are explored. 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. This research encompasses three inter-related issues: (i) analysis and modeling of speech characteristics brought on by workload task stress, speaker emotion/stress or speech produced in noise (Lombard effect), (ii) adaptive signal processing methods tailored to speech enhancement and stress equalization, and (iii) formulation of new recognition algorithms which are robust in adverse environments. An overview of a statistical analysis of a Speech Under Simulated and Actual Stress (SUSAS) database is presented. This study was conducted on over 200 parameters in the domains of pitch, duration, intensity, glottal source and vocal tract spectral variations. These studies motivate the development of a speech modeling approach entitled Source Generator Framework in which to represent the dynamics of speech under stress. This framework provides an attractive means for performing feature equalization of speech under stress. In the second half of this paper, three novel approaches for signal enhancement and stress equalization are considered to address the issue of recognition under noisy stressful conditions. The first method employs (Auto:I,LSP:T) constrained iterative speech enhancement to address background noise and maximum likelihood stress equalization across formant location and bandwidth. The second method uses a feature enhancing artificial neural network which transforms the input stressed speech feature set during parameterization for keyword recognition. The final method employs morphological constrained feature enhancement to address noise and an adaptive Mel-cepstral compensation algorithm to equalize the impact of stress. Recognition performance is demonstrated for speech under a range of stress conditions, signal-to-noise ratios and background noise types.