An HMM Acoustic Model Incorporating Various Additional Knowledge Sources

We introduce a method of incorporating additional knowledge sources into an HMM-based statistical acoustic model. The probabilistic relationship between information sources is first learned through a Bayesian network to easily integrate any ad-ditional knowledge sources that might come from any domain and then the global joint probability density function (PDF) of the model is formulated. Where the model becomes too com-plex and direct BN inference is intractable, we utilize a junction tree algorithm to decompose the global joint PDF into a linked set of local conditional PDFs. This way, a simplified form of the model can be constructed and reliably estimated using a limited amount of training data. Here, we apply this framework to in-corporate accents, gender, and wide-phonetic knowledge infor-mation at the HMM phonetic model level. The performance of the proposed method was evaluated on an LVCSR task using two different types of accented English speech data. Experi-mental results revealed that our method improves word accu-racy with respect to standard HMM. Index Terms: acoustic model, knowledge incorporation, Bayesian network, junction tree algorithm, wide-phonetic knowledge. 1