On a new predictor for the waveform coding of speech signal by using the dual autocorrelation and the sigma-delta technique

Speech waveforms are highly correlated between the adjacent samples. One way of increasing the correlation in speech signals is to simply integrate the input signal prior to coding. The integrated values can be removed by conventional differentiation at the receiver. This emphasizes the low frequencies of speech signals and increases the correlation between adjacent samples. The above arrangement is called as a sigma-delta technique. In this paper, we propose a new predictor which uses such characteristics of dual autocorrelation and the sigma-delta technique. That is, we integrate input signals prior to coding, and then predict the present integrate sample by using two samples, one past and one next. The proposed predictor has higher mean prediction gain of 8.65 dB than that of the CCITT-Recommendation ADPCM