LPC analysis/Synthesis from speech inputs containing quantizing noise or additive white noise

An important problem in some communication systems is the performance of linear prediction (LPC) analysis with speech inputs that have been corrupted by (signal-correlated) quantization distortion or additive white noise. To gain a first insight into this problem, a high-quality speech sample was deliberately degraded by using various degrees (bit rates of 16 kbps and more) of differential PCM (DPCM), and delta modulation (DM) quantization, and by the introduction of additive white noise. The resulting speech samples were then analyzed to obtain the LPC control signals: pitch, gain, and the linear prediction coefficients. These control parameters were then compared to the parameters measured in the original, high quality signal. The measurements of pitch perturbations were assessed on the basis of how many points exceeded an appropriate difference limen. A distance measure proposed by Itakura was used to compare the original LPC coefficients with the coefficients measured from the degraded speech. In addition, the measured control signals were used to synthesize speech for perceptual evaluation. Results suggest that LPC analysis/synthesis is fairly immune to the degradation of DPCM quantization. The effects of DM quantization are more severe and the effects of additive white noise are the most serious.