Waveform speech coding using multiscale recurrent patterns

This paper revisits the waveform paradigm for coding speech signals, using a multiscale recurrent-pattern matching approach. The so-called MMP (Multidimensional Multiscale Parser) algorithm uses a dictionary which is constantly updated with expansions, contractions, and concatenations of previously encoded segments. This provides a learning ability to the MMP, particularly suited for coding voiced and silent segments of speech. Additional features (nonuniform and auxiliary displacement dictionaries) are considered in order to adjust the MMP learning mechanism for the speech coding problem. Current MMP algorithm achieves a fair-to-good objective score when operating at 8 kbps, as indicated by several speech-coding experiments. This indicates that it may be worthy to further investigate the use of the multiscale recurrent pattern matching paradigm for speech coding.