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Study of integration of statistical model-based voice activity detection and noise suppression

This paper addresses robust front-end processing for automatic speech recognition (ASR) in noisy environments. To recognize the corrupted speech accurately, it is necessary to employ robust methods against various types of interference. Usually, noise suppression (NS) is used for the front-end processing of ASR in noise. Voice activity detection (VAD) is also used for front-end processing to reduce the redundant non-speech period. VAD and NS are typically combined as series processing. However, VAD and NS should not be assumed to be a separate technique, because the output information of these methods be mutually beneficial. Thus, we investigate the integrated front-end processing of VAD and NS, which can utilize each others’ inputoutput information. The evaluation is carried out by using a concatenated speech corpus, CENSREC-1-C. In the evaluation, the proposed method improves ASR accuracy compared with conventional series combination.