Partial-tied-mixture Auxiliary Chain Models for Speech Recognition Based on Dynamic Bayesian Networks

It is observed that the cepstral-based features used for speech recognition are sensitive to some auxiliary information (e.g. pitch). Encoding the auxiliary information in discrete auxiliary variables based on dynamic Bayesian networks (DBNs) typically results in an increased number of parameters. There are tradeoffs to be studied between parameter reduction and dependency modeling. In this paper, we propose a method using state-specific partial tying with information- theoretic dependency selection. This method is essentially to relax the conditional independence assumptions imposed by the full-tied-mixture model, by adding strong dependencies (i.e. those with large mutual information computed from training data). Experiments were carried out on the OGI Numbers database, considering pitch as the auxiliary information. The results show that the partial-tied-mixture auxiliary chain models can efficiently improve recognition performances with an economical way of increasing parameters.