Incorporating phonetic knowledge into a multi-stream HMM framework

This paper presents a technique for improving the performance of multi-stream HMMs in ASR systems. In this technique stream exponents of the multi-stream model are chosen with respect to the phonological content of the underlying states. Two distinctive feature sets namely MFCCs and formant-like features are used for investigating the potential of this technique. The experiments are performed on the AURORA database under the distributed speech recognition (DSR) framework. The proposed front-end constitutes an alternative to the DSR-XAFE (XAFE : eXtended Audio Front-End) provided by European Telecommunications Standards Institute. It is shown that the results obtained from the proposed method leads to improvement up to 10% in word accuracy relative to the word accuracy obtained form the multi-stream model with tied exponents and up to 35% relative improvement in word accuracy over the state-of-the-art MFCC-based system.