Application of triphone clustering in acoustic modeling for continuous speech recognition in Bengali

The performance of the acoustic models is highly reflective on the overall performance of any continuous speech recognition system. Hence generation of an accurate and robust acoustic model holds the key to satisfactory recognition performance. As phones are found to vary according to the position of occurrence within a particular word, context information is of prime importance in acoustic modeling of phonetic signals. In this paper we look at the effect of triphone-based acoustic modeling over monophone based acoustic models in the context of continuous speech recognition in Bengali. Keeping in mind the lack of training resources for triphone-based acoustic modeling in Bengali, we have also described herein, the method of generating triphone clusters using decision tree based techniques. These triphone clusters have then been used to generate tied-state triphone based acoustic models to be used in a continuous speech recognizer.