Quantization of predictor coefficients in speech coding
John Grass · 1990
This thesis examines techniques of efficiently coding Linear Predictive Coding (LPC) coefficients with 20 to 30 bits per 20 ms speech frame. Scalar quantization is the first approach evaluated. Results show that Line Spectral Frequencies require significantly fewer bits than reflection coefficients for comparable performance. The second approach investigated is the use of vector-scalar quantization. In the first stage, vector quantization is performed. The second stage consists of a bank of scalar quantizers which code the vector errors between the original LPC coefficients and the components of the vector of the quantized coefficients. The approach is to couple the vector and scalar quantization stages. Every codebook vector is compared to the original LPC coefficient vector to produce error vectors. The second innovation into vector-scalar quantization is the incorporation of a small adaptive codebook to the large fixed codebook. Frame-to-frame correlation of the LPC coefficients is exploited at no extra cost in bits. The performance of the vector-scalar quantization using the two new techniques is better than that of the scalar coding techniques currently used in conventional LPC coders.