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Boves, L.W.J.

Veth, J.M. de

In this paper we propose an extension to the classical RASTA technique. The new method consists of classical RASTA filtering followed by a phase correction operation. In this manner, the influence of the communication channel is as effectively removed as with classical RASTA. However, our proposal does not introduce a left-context dependency like classical RASTA. Therefore the new method is better suited for automatic speech recognition based on context-independent modeling with Gaussian mixture hidden Markov models. We tested this in the context of connected digit recognition over the phone. In case we used context-dependent hidden Markov models (i.e. word models), we found that classical RASTA and phase-corrected RASTA performed equally well. For context-independent phone-based models, we found that phase-corrected RASTA can outperform classical RASTA depending on the acoustic resolution of the models. 1. INTRODUCTION  For automatic speech recognition (ASR) over the telephone it is w..

Johan de Veth

Louis Boves

Phase-corrected RASTA for automatic speech recognition over the phone

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