September 22-25, 1997
This paper addressed the problem of speech signal pre-classification 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.
Bibliographic reference. Hong, Wei-Tyng / Chen, Sin-Horng (1997): "A robust RNN-based pre-classification for noisy Mandarin speech recognition", In EUROSPEECH-1997, 1083-1086.