Context dependent quantization for distributed and/or robust speech recognition

It is well-known that the high correlation existing in speech signals is very helpful in various speech processing applications. In this paper, we propose a new concept of context-dependent quantization, in which the representative parameter (whether a scalar or a vector) for a quantization partition cell is not fixed, but depends on the signal context on both sides, and the signal context dependencies can be trained with a clean speech corpus or estimated from a noisy speech corpus. This results in a much finer quantization based on local signal characteristics, without using any extra bit rate. This approach is equally applicable to all (scalar or vector) quantization approaches, and can be used either for signal compression in distributed speech recognition (DSR) or for feature transformation in robust speech recognition. In the latter case, each feature parameter is simply transformed into its representative parameter after quantization. In preliminary experiments with AURORA 2 and simulated GPRS channels, this concept is integrated with a recently proposed Histogram-based Quantization (HQ), the partition cells of which are also dynamic depending on local signal statistics. Significant performance improvements were obtained with the presence of both environmental noise and transmission errors.