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Abstract

Apparatus for processing acoustic features extracted from a sample of speech data forming a feature vector signal every frame period includes a first linear prediction analyzer, a vector quantizer, at least one partitioned vector quantizer and a scalar quantizer. The first linear prediction analyzer performs a linear prediction analysis on the feature vector signal to generate a first error vector signal. Next, the vector quantizer performs a vector quantization on the first error signal thereby generating a first index corresponding to a first prestored vector signal which is an approximation of the first error vector signal. The vector quantizer also generates a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal. Next, the at least one partitioned vector quantizer performs a partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index corresponding to a second prestored vector signal which is an approximation of the first portion of the residual vector signal. Next, the scalar quantizer performs a scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal. The first, second and third indices are combined to form an encoded vector signal which is a compressed representation of the feature vector signal. The encoded vector signal may be transmitted and/or stored as desired. The feature vector signal may be reconstructed from the encoded vector signal by adding the corresponding prestored signals to the encoded vector signal to form a decompressed representation of the feature vector signal.

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