Noise and late reverberation reduction algorithms were compared by means of objective speech quality and speech recognition accuracy (Acc%) measures. Negative effects of excessive noise reduction for automatic speech recognition (ASR) had been shown. It was found possibility of improvement the noise suppression algorithms quality, in terms of Acc%, by proper choice of a priori signal-to-noise assessment technique. It was shown that decision-directed technique is the best for speech quality, when “rough” assessment technique is the best for ASR, and the maximum likelihood technique occupies an intermediate position. When studying late reverberation suppression algorithms, it was found existence of optimal, in terms of Acc%, parameters values of the algorithms. It was shown also that these parameters values are different for ASR and for speech enhancement. Thus, late reverberation suppression algorithms behavior is similar to one of noise suppression algorithms. Study of speech quality measures had showed that only few of them were in good agreement with Acc%. But existence of such measures is very important, because it enables use them instead of Acc% and, thus, enables essentially simplify assessment of noise and reverberation robustness in ASR.
Noise, Late Reverberation, Reduction, Algorithm, Speech Quality Measure, Automatic Speech Recognition Accuracy
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