A combination of discriminative and maximum likelihood techniques for noise robust speech recognition

We study how discriminative and maximum likelihood (ML) techniques should be combined in order to maximize the recognition accuracy of a speaker-independent automatic speech recognition (ASR) system that includes speaker adaptation. We compare two training approaches for the speaker-independent case and examine how well they perform together with four different speaker adaptation schemes. In a noise robust connected digit recognition task we show that the minimum classification error (MCE) training approach for speaker-independent modelling together with the Bayesian speaker adaptation scheme provide the highest classification accuracy over the whole lifespan of an ASR system. With the MCE training we are capable of reducing the recognition errors by 30% over the ML approach in the speaker-independent case. With the Bayesian speaker adaptation scheme we can further reduce the error rates by 62% using only as few as five adaptation utterances