Hidden Markov models applied to very low bit rate speech coding

A new type of very low bit rate speech coder based on a global Discrete Hidden Markov Model (DHMM) of continuous speech for a single speaker is presented here. Several important issues of the training, coding, and decoding procedures are discussed for a 64-state, 1024-observation model. Such a framework is useful in reducing the redundancy in a 10-bit classical Vector Quantizer (VQ), and could lead to a DHMM coder with a bit rate comparable to that of a Segment Vocoder (SV) or a Matrix Quantizer (MQ). This is achieved not only by modelling the long term non-stationarity and the inter-frame time dependencies of the speech, but also by efficiently representing a different kind of information such as vocal tract structure and linguistic patterns.