Frame-discriminative and confidence-driven adaptation for LVCSR

Maximum likelihood linear regression (MLLR) has become the most popular approach for adapting speaker-independent hidden Markov models to a specific speaker's characteristics. However, it is well known, that discriminative training objectives outperform maximum likelihood training approaches, especially in cases where training data is very limited, as it always is the case in adaptation tasks. Therefore, this paper explores the application of a frame-based discriminative training objective for adaptation. It presents evaluations for supervised as well as for unsupervised adaption on the 1993 WSJ adaptation tests of native and non-native speakers. Relative improvements in word error rate of up to 25% could be measured compared to the MLLR adapted recognition systems. Along with unsupervised adaptation, the paper also presents the improvements achieved by the application of confidence measures. They provided an average relative improvement of 10% compared to ordinary unsupervised MLLR.