Cluster-Dependent Acoustic Modeling

In this paper, we present cluster-dependent acoustic modeling for large-vocabulary speech recognition. With large amount of acoustic training data, we build multiple cluster-dependent models (CDM), each focusing on a group of speakers in order to represent speaker-dependent characteristics. It is motivated by the fact that a sufficiently trained speaker-dependent (SD) model is better than the speaker-independent (SI) model. During decoding, we decode the data of each test speaker using CDMs selected under certain criteria to achieve high recognition accuracy. Various speaker clustering and model selection techniques are proposed and compared in the task of Broadcast News (BN) transcription. The CDM provided more than 1% absolute gain in unadapted decoding and 0.5% gain in adapted decoding when compared to our baseline system on the EARS BN 2003 development test set.