Two-dimensional multi-resolution analysis of speech signals and its application to speech recognition

This paper describes a novel approach of using multi-resolution analysis (MRA) for automatic speech recognition. Two-dimensional MRA is applied to the short-time log spectrum of speech signal to extract the slowly varying spectral envelope that contains the most important articulatory and phonetic information. After passing through a standard cepstral analysis process, the MRA features are used for speech recognition in the same way as conventional short-time features like MFCCs, PLPs, etc. Preliminary experiments on both clean connected speech and noisy telephone conversation speech show that the use of MRA cepstra results in a significant reduction in insertion error when compared with MFCCs.