Abstract
The paper describes an auditory processing-based feature extraction strategy for robust speech recognition in environments, where conventional automatic speech recognition (ASR) approaches are not successful. It incorporates a combination of gammatone filtering, modulation spectrum and non-linearity for feature extraction in the recognition chain to improve robustness, more specifically the ASR in adverse acoustic conditions. The experimental results with standard Aurora-4 large vocabulary evaluation task revealed that the proposed features provide reliable and considerable improvement in terms of robustness in different noise conditions and are comparable to those of standard feature extraction techniques.
Highlights
Present technological advances in speech processing systems aim at providing robust and reliable interfaces for practical deployment
Additive noise from interfering noise sources and convolutive noise arising from acoustic environment and transmission channel characteristics mostly contribute to the degradation of speech intelligibility as well as the performance of speech recognition systems
This article addresses the problem of achieving robustness in large vocabulary automatic speech recognition (ASR) systems by incorporating principles inspired by cochlea processing in the human auditory system
Summary
Present technological advances in speech processing systems aim at providing robust and reliable interfaces for practical deployment. The gammatone filter bank with non-uniform bandwidths and non-uniform spacing of center frequencies provided better robustness in adverse noise conditions for speech recognition tasks [12,13,14,15]. Another important characteristic, the modulation spectrum of speech, represents low temporal modulation components and is important for speech intelligibility [16,17]. The effects of rectification, non-linearities, short-term adaptation and low-pass filtering were shown to contribute the most to robustness at low SNRs. In another study [8], the techniques motivated by human auditory processing are shown to improve the accuracy of automatic speech recognition systems.
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