Reduced Channel Dependence for Speech Recognition

Hy Murveit, John Butzberger, Mitch Weintraub

Abstract: Speech recognition systems tend to be sensitive to unimportant steady-state variation in speech spectra (i.e. those caused by varying the microphone or channel characteristics). There have been many attempts to solve this problem; however, these techniques are often computationally burdensome, especially for real-time implementation. Recently, Hermansy et al. [1] and Hirsch et al. [2] have suggested a simple technique that removes slow-moving linear channel variation with little adverse effect on speech recognition performance. In this paper we examine this technique, known as RASTA filtering, and evaluate its performance when applied to SRI's DECIPHER™ speech recognition system [3]. We show that RASTA filtering succeeds in reducing DECIPHER™'s dependence on the channel.