On the use of normalized LPC error towards better large vocabulary speech recognition systems
Rathinavelu Chengalvarayan · 2002
Linear prediction (LP) analysis is widely used in speech recognition for representing the short time spectral envelope information of speech. The predictive residues are usually ignored in LP analysis based speech recognition system. In this study, the normalized residual error based on LP is introduced and the performance of the recognizer has been further improved by the addition of this new feature along with its first and second order derivative parameters. The convergence property of the training procedure based on the minimum classification error (MCE) approach is investigated, and experimental results on the city name recognition task demonstrated a 8% string error rate reduction by using the extended feature set as compared to conventional feature set.