Application Research Of Laguerre Filter In Frontend Processing Of Speech Recognition

Speech recognition has become a hotspot in the field of speech signal processing. But it is not easy to solve perfectly because of its complexity, especially for the speech recognition of Speaker Independent in noisy environment. This paper introduced the fundamental of speech recognition and discussed some commonly used feature extraction methods and specially analysed the ZCPA feature extraction based on a classical speech recognition system. Based on above conclusion, it presented a method which improved the front-end processing of ZCPA feature extraction, and got the better recognition rate which has excellent anti-noise properties.The filter realized by Laguerre network is a compromise between the FIR and IIR. It not only possessed the stability of FIR, but also had the good property of pass-band and stop-band of IIR, and achieved a long time memory. In this paper, Laguerre filter's design approach was performed by evaluating the filter parameter employed Newton-Raphson method and corresponding Laguerre coefficients and obtaining optimum filter employed Canchy theorem when the minimum-mean-square-error of the frequency response between the Laguerre filter and the optimum filter was existed. It designed Laguerre filter and compared the frequency response of Laguerre filter with the frequency response of conventional FIR and IIR filters through experiment, educed Laguerre filter has small length and appropriate linear phase with less ripples in pass-band and stop-band. But the cost is the complex computation which can be reduced by the introduced lemma.Based on the better property of Laguerre filter, it used Laguerre filter replacing the traditional FIR filter in the ZCPA feature extraction, and employed RBF network and HMM to train and recognise in the back-end. The experiment results showed applying Laguerre filter replacing the traditional FIR filter in feature extraction would improved speech recognition rate and anti-noise properties.At last, it combined Laguerre transform and wavelet transform based on the frequency warped properties of Laguerre transform and multiresolution of wavelet transform, detailedly introduced its structure, simultaneously also explained what is the difficulty, and the further task is applying the frequency warped wavelet transform to the feature extraction, it's expect to gain the better speech recognition result.