Model-space compensation of microphone and noise for speaker-independent speech recognition

Ambient noise (additive distortion) and microphone changes (convolutive distortion) are two sources of distortion that may severely degrade speech recognition performance in real operating environments. Simultaneously modeling the two distortion sources has been a great challenge for robust speech recognition. A method, called JAC (Joint compensation of Additive and Convolutive distortions), is presented. It uses two log-spectral domain components in speech acoustic models to represent additive and convolutive distortions. The method adapts HMM mean vectors with a noise estimate and a channel estimate. The noise estimate is calculated from the pre-utterance pause and the channel estimate is calculated using an EM algorithm from speech utterances produced in the distortion environment. Evaluated on a noisy speech database recorded in-vehicle with a hands-free distant microphone in several driving conditions, the algorithm reduces recognition word error rate in typical operating conditions by an order of magnitude.