Robust speech recognition with spectral subtraction in low SNR
نویسندگان
چکیده
Speech recognition in noisy environments is a very difficult task. It is is desirable to search for parameters that would relate the speech enhancement technique directly with the recognizer to optimize the recognition performance. In this paper, Noise Reduction Rate (NRR) and Mel Cepstrum Distortion (MelCD) are investigated when using Spectral Subtraction (SS). Under low SNR such as 0dB,5dB and 10dB, maximizing NRR nor minimizing the MelCD does not result in a better recognition performance. Thus, the conventional SS in which the oversubtraction parameter is a function of SNR renders to be ineffective in the point-of-view of the recognizer. Our proposed method derives for SS directly from the training utterances used in creating the Hidden Markov Models (HMM) that optimizes the recognition performance. By superimposing office noise to the SS-denoised noisy speech, we achieved 26.0% and 7.6% for relative increase in word accuracy for the proposed matched and generalized respectively.
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