Poor speech recognition method based on support vector machine

TL;DR: In this paper, a poor speech recognition method based on a support vector machine (SVM) classifier was proposed, which comprises the steps of firstly acquiring an input voice stream, decoding the input voice streams to be an original voice signal, and performing the preprocessing operation; conducting windowing and framing processing for the voice data after being preprocessed; extracting shifting difference cepstrum parameter characteristics from each frame speech; classifying shifting difference parameter characteristics by utilizing a Gaussian mixture model; then classifying candidate frames of the classified poor speech by utilizing SVM class

Abstract: The invention relates to a poor speech recognition method based on a support vector machine. The method comprises the steps of firstly acquiring an input voice stream, decoding the input voice stream to be an original voice signal, and performing the preprocessing operation; conducting windowing and framing processing for the voice data after being preprocessed; extracting shifting difference cepstrum parameter characteristics from each frame speech; classifying shifting difference cepstrum parameter characteristics by utilizing a Gaussian mixture model; then classifying candidate frames of the classified poor speech by utilizing a support vector machine (SVM) classifier, and confirming a final poor speech frame; and extracting a poor speech fragment according to the quantity of the poor speech frames within a given time to be stored. Multiple Gaussian mixture models can realize the quick classification to extract the candidate poor speech frames, and the classification accuracy can be improved by the support vector machine classifier.