An important problem in speech coding is the quantization of linear predictive coefficients (LPC) with the smallest possible number of bits while maintaining robustness to a large variety of speech material and transmission media. Since direct quantization of LPC’s is known to be unsatisfactory, we consider this problem for an equivalent representation, namely, the line spectral frequencies (LSF). To achieve an acceptable level of distortion a scalar quantizer for LSF’s requires a 36 bit codebook. We derive a 30 bit two-quantizer scheme which achieves a performance equivalent to this scalar quantizer. This equivalence is verified by tests on data taken from various types of filtered speech, speech corrupted by noise and by a set of randomly generated LSF’s. The two-quantizer format consists of both a vector and a scalar quantizer such that for each input, the better quantizer is used. The vector quantizer is designed from a training set that reflects the joint density (for coding efficiency) and which ensures coverage (for robustness). The scalar quantizer plays a pivotal role in dealing better with regions of the space that are sparsely covered by its vector quantizer counterpart. A further reduction of 1 bit is obtained by formulating a new adaptation algorithm for the vector quantizer and doing a dynamic programming search for both quantizers. The method of adaptation takes advantage of the ordering of the LSF’s and imposes no overhead in memory requirements. The dynamic programming search is feasible due to the ordering property. Subjective tests in a speech coder reveal that the 29 bit scheme produces equivalent perceptual quality to that when the parameters are unquantized.
All Science Journal Classification (ASJC) codes
- Acoustics and Ultrasonics
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering