Spectral Autocorrelation Technology for Speech Recognition
Jing Xu · Shuju caiji yu chuli · 2004
A linear predictive analysis method of speech recognition for estimating sample autocorrelation from the speech signal spectral envelope is proposed based on spectral autocorrelation. To obtain spectral envelope from estimating frequency samples a frequency normalization can be applied to the estimated spectral envelope. The spectral envelope is the mel frequency scale and IDFT is used to extract the estimate of sample autocorrelations. The cepstral coefficients are obtained from sampling autocorrelation results. HMM experiments show that cepstral coefficients improve the performances of the recognizer at low R SN . The recogniton rate is improved more than 10% and it works well in noise environments.