A unified approach of incorporating general features in decision tree based acoustic modeling

A unified maximum likelihood framework of incorporating phonetic and non-phonetic features in decision tree based acoustic modeling is proposed. Unlike phonetic features, non-phonetic features in this context are those features, which cannot be derived from the phoneme identities. Although non-phonetic features are used in speech recognition, they are often treated separately and based on various heuristics. In our approach, non-phonetic features are included as additional tags to the decision tree clustering. Moreover, the proposed tagged decision tree is based on the full training data, and therefore, it alleviates the problem of training data depletion in building specific feature dependent acoustic models. Experimental results indicate that up to 10% word error rate reduction can be achieved in a large vocabulary (Wall Street Journal) speech recognition task based on the proposed approach.