Knowledge-based and automated clustering in MLLR adaptation of acoustic models for LVCSR

This paper describes the analysis of the performance of MLLR-based speaker adaptation in a large vocabulary continuous speech recognition system. Two different approaches of clustering in MLLR-adaptation with more regression classes, knowledge-based clustering and automatic clustering were analysed. The contribution of mentioned acoustic model adaptation using these two clustering approaches were compared based on the word error rate ratio (WERR) of target LVCSR. Realized study proved that the knowledge-based clustering may bring improvement comparable to the tree-based clustering, when only a few transformation classes are manually defined.