Speech Analysis and Synthesis by Linear Prediction of the Speech Wave

TLDR

The authors present a method for efficiently encoding speech by representing it with time‑varying vocal‑tract transfer‑function parameters and excitation characteristics, aiming to enable compact transmission and storage. The method analyzes 10‑kHz speech samples using 12‑tap linear prediction, derives 15 parameters—including predictor coefficients, pitch period, voiced‑unvoiced flag, and rms—by minimizing mean‑squared error, and synthesizes the waveform with a linear recursive filter driven by either quasiperiodic pulses or white noise. The approach enables efficient transmission and storage of speech signals and facilitates extraction of formant frequencies, bandwidths, spectral envelope, and autocorrelation functions.

Abstract

We describe a procedure for efficient encoding of the speech wave by representing it in terms of time-varying parameters related to the transfer function of the vocal tract and the characteristics of the excitation. The speech wave, sampled at 10 kHz, is analyzed by predicting the present speech sample as a linear combination of the 12 previous samples. The 12 predictor coefficients are determined by minimizing the mean-squared error between the actual and the predicted values of the speech samples. Fifteen parameters—namely, the 12 predictor coefficients, the pitch period, a binary parameter indicating whether the speech is voiced or unvoiced, and the rms value of the speech samples—are derived by analysis of the speech wave, encoded and transmitted to the synthesizer. The speech wave is synthesized as the output of a linear recursive filter excited by either a sequence of quasiperiodic pulses or a white-noise source. Application of this method for efficient transmission and storage of speech signals as well as procedures for determining other speech characteristics, such as formant frequencies and bandwidths, the spectral envelope, and the autocorrelation function, are discussed.