Adaptive Preprocessing for Linear Predictive Speech Compression Systems

In any vocoder, the process of parameter quantization is crucial for reducing the transmission rate while maintaining the quality of the synthesized speech. We have observed that, for linear predictive vocoders, the short-time spectral dynamic range of the speech signal is the single most important factor that affects the quantization properties of transmission parameters, irrespective of the particular choice of parameters. We describe two preprocessing methods by which the spectral dynamic range is reduced, thereby improving quantization properties. In the first method, adaptive optimal (linear predictive) preemphasis is applied either directly to the speech signal or more easily to the autocorrelation coefficients through convolution. Experiments indicate that an appropriate set of preemphasis filters can be pre-selected; for any speech segment being analyzed, the filter from this set that is closest to the optimal choice can be used for preemphasis. The advantages of such a scheme as well as the effect of first- and second-order preemphasis on quantization and speech quality are discussed. The second method of preprocessing (the SIGMA method) involves multiplying the impulse response of the inverse linear prediction filter by an exponential window. Some preliminary results obtained using this method are reported.