Improving Speech Recognition Performance via

This paper presents improvements in acoustic and lan-guage modeling for automatic speech recognition. Specif-ically, semi-continuous HMMs (SCHMMs) with phone-dependent VQ codebooks are presented and incorporated into the SPHINX-II speech recognition system. The phone-dependent VQ codebooks relax the density-tying constraint in SCHMMs in order to obtain more detailed models. A 6 % error rate reduction was achieved on the speaker-independent 20,000-word Wall Street Journal (WSJ) task. Dynamic adaptation of the language model in the context of long documents is also explored. A maximum entropy framework is used to exploit long distance trigrams and trigger effects. A 10 % – 15 % word error rate reduction is reported on the same WSJ task using the adaptive language modeling technique.