Speech Communication - Eurospeech '91
Data-driven environmental compensation for speech recognition: a unified approach
Speech Communication
Combining speech enhancement and auditory feature extraction for robust speech recognition
Speech Communication - Special issue on noise robust ASR
Assessing local noise level estimation methods: application to noise robust ASR
Speech Communication - Special issue on noise robust ASR
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Speech recognition in noisy environments
Speech recognition in noisy environments
Speech enhancement based conceptually on auditory evidence
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
The use of phase in complex spectrum subtraction for robust speech recognition
Computer Speech and Language
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Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios.