A dynamic parameter compensation method for noisy speech recognition

Model-based compensation techniques have been successfully used for speech recognition in noisy environments. Popular model-based compensation methods such as the Log-Normal PMC and Log-Add PMC generally use approximate compensation for dynamic parameters. Hence their recognition accuracy is degraded at low and very low signal-to-noise ratios. In this paper we use time derivatives of static features to derive a dynamic parameter compensation method (DPCM). In this method, we assume the static features independent of the dynamic features of speech and noise. This assumption helps simplify the procedures of the compensation of delta and delta–delta parameters. The new compensated dynamic model together with any known compensated static model form a new corrupted speech recognition model. Experimental results show that the recognition model using this DPCM scheme gives recognition accuracy better than the original model compensation method for different additive noises at the expense of slight increase in computational complexity.