Combining speech enhancement and auditory feature extraction for robust speech recognition
A major deficiency in state-of-the-art automatic speech recognition (ASR) systems is the lack of robustness in additive and convolutional noise. The model of auditory perception (PEMO), developed by Dau et al. (T. Dau, D. Pschel, A. Kohlrausch, J. Acoust. Soc. Am. 99 (6) (1996) 36153622) for psychoacoustical purposes, partly overcomes these difficulties when used as a front end for automatic speech recognition. To further improve the performance of this auditory-based recognition system in background noise, different speech enhancement methods were examined, which have been evaluated in earlier studies as components of digital hearing aids. Monaural noise reduction, as proposed by Ephraim and Malah (Y. Ephraim, D. Malah, IEEE Trans. Acoust. Speech Signal Process. ASSP-32 (6) (1984) 11091121) was compared to a binaural filter and dereverberation algorithm after Wittkop et al. (T. Wittkop, S. Albani, V. Hohmann, J. Peissig, W. Woods, B. Kollmeier, Acustica United with Acta Acustica 83 (4) (1997) 684699). Both noise reduction algorithms yield improvements in recognition performance equivalent to up to 10 dB SNR in non-reverberant conditions for all types of noise, while the performance in clean speech is not significantly affected. Even in real-world reverberant conditions the speech enhancement schemes lead to improvements in recognition performance comparable to an SNR gain of up to 5 dB. This effect exceeds the expectations as earlier studies found no increase in speech intelligibility for hearing-impaired human subjects.
speech enhancement robust speech recognition perceptive modeling auditory front end