V. Mitra, H, Franco, M. Graciarena, and A. Mandal, “Normalized amplitude modulation features for large vocabulary noise-robust speech recognition,” in Proc. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), pp. 4117–4120.
Abstract
Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated systems. A traditional way to add robustness to a speech recognition system is to construct a robust feature set for the speech recognition model. In this work, we present an amplitude modulation feature derived from Teager’s nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features…
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