Speaker adaptation in the Philips system for large vocabulary continuous speech recognition

The combination of Maximum Likelihood Linear Regression (MLLR) with Maximum a posteriori (MAP) adaptation has been investigated for both the enrollment of a new speaker as well as for the asymptotic recognition rate after several hours of dictation. We show that a least mean square approach to MLLR is quite e ective in conjunction with phonetically derived regression classes. Results are presented for both ARPA read-speech test sets and real-life dictation. Signi cant improvements are reported. While MLLR achieves a faster adaptation rate when only few data is available, MAP has desirable asymptotic properties and the combination of both methods provides the best results. Both incremental and iterative batch modes are studied and compared to the performance of speaker-dependent training.