Extraction of LP-based features from one-bit quantized speech signals for recognition purposes

A simplified fixed-point computation of cepstral coefficients, based on linear predictive analysis and infinite clipping of speech signals, is described. The autocorrelation function of the clipped signal is directly used to compute the linear predictor coefficients. The performance of an isolated word recognition system based on these coefficients is presented and compared with a system which uses standard linear predictive cepstral features. The results show that these coefficients can be efficiently used for small dictionary speech recognition systems and, since the analog-to-digital conversion can be avoided, they are suitable for a low-voltage and low-power hardware implementation.