On the efficiency of classical RASTA filtering for continuous speech recognition: Keeping the balance between acoustic pre-processing and acoustic modelling ?
The efficiency of classical RASTA filtering for channel normalisation was investigated for continuous speech recognition based on context-independent and context-dependent hidden Markov models. For a medium and a large vocabulary continuous speech recognition task, recognition performance was established for classical RASTA filtering and compared to using no channel normalisation, cepstrum mean normalisation, and phase-corrected RASTA. Phase-corrected RASTA is a technique that consists of classical RASTA filtering followed by a phase correction operation. In this manner, channel bias is as effectively removed as with classical RASTA. However, for phase-corrected RASTA, amplitude drift towards zero in stationary signal portions is diminished compared to classical RASTA. The results show that application of classical RASTA filtering resulted in decreased recognition performance when compared to using no channel normalisation for all conditions studied, although the decrease appeared to be smaller for context-dependent models than for context-independent models. However, for all conditions, recognition performance was significantly and substantially improved when phase-corrected RASTA was used and reached the same performance level as obtained for cepstrum mean normalisation in some cases. It is concluded that classical RASTA filtering can only be effective for channel robustness, if the impact of the amplitude drift towards zero can be kept as limited as possible.
classical rasta filtering continuous speech recognition channel normalisation phase-corrected rasta telephone lines