A robust RNN-based pre-classification for noisy Mandarin speech recognition

This paper addressed the problem of speech signal preclassification for robust noisy speech recognition. A novel RNN-based pre-classification scheme for noisy Mandarin speech recognition is proposed. The RNN, which is trained to be insensitive to noise-level variation, is employed to classify each input frame into the three broad classes of initial, final and pure-noise. An on-line noise tracking and estimation for noise model compensation is then performed. Besides, a broad-class likelihood compensation based on the RNN outputs is also performed to help the recognition. Experimental results showed that a significant improvement on syllable recognition rate has been achieved under non-stationary noise environment.