In this paper we address the issue of pronunciation modeling for conversational speech synthesis. We experiment with two different HMM topologies (fully connected state model and forward connected state model) for sub-phonetic modeling to capture the deletion and insertion of sub-phonetic states during speech production process. We show that the experimented HMM topologies have higher log likelihood than the traditional 5-state sequential model. We also study the first and second mentions of content words and their influence on the pronunciation variation. Finally we report phone recognition experiments using the modified HMM topologies.

This paper presents improvements in acoustic and language modeling for automatic speech recognition. Specifically, semi-continuous HMMs (SCHMMs) with phonedependent VQ codebooks are presented and incorporated into the SPHINX-II speech recognition system. The phonedependent 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 speakerindependent 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.