Abstract
In this paper, the improved linear predictive coding (LPC) coefficients of the frame are employed in the feature extraction method. In the proposed speech recognition system, the static LPC coefficients + dynamic LPC coefficients of the frame were employed as a basic feature. The framework of linear discriminant analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric speech vector space for the speech recognition system. Using the continuous hidden Markov model (HMM) as the speech recognition model, the speech recognition system was successfully constructed. Experiments are performed on the isolated-word speech recognition task. It is found that the improved LPC feature extraction method is quite efficient.
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