Research On Algorithms Of Speech Endpoint Detection

Currently, in the age of information technology, technologies include speech recognition and speech encoding and speech enhancement would be being provided strong support in the security field, also the field of human-computer interaction and the communication field and the future of the field of consumer electronics products. The purpose of speech endpoint detection is to analysis the pure speech signal and mute segments from a speech signal accurately. This technology will directly affect the performance of the technology of speech recognition and the efficiency of speech encoding.A complete model of speech endpoint detection can be divided into three parts. Firstly, signal pretreatment. Secondly, speech signal feature extraction, now, we rely on the principle of multi-resolution analysis of wavelet analysis(WA) to extract the feature of speech signal. Finally, speech endpoint detection model establishment. Some traditional Speech endpoint detection algorithms are introduced, such as the double threshold method which is based on time domain, the general entropy method which is based on frequency domain, and the method which is based on the characteristics of the reverse.In order to get satisfactory result in low SNR and complex noise environment, the endpoint detection algorithm which based on extreme learning machine(ELM) is proposed,and the algorithm is further optimized to make up for the defects of the algorithm itself.(1)In order to optimize the ELM neural network’s input weights and hidden layer deviations, the endpoint detection model based on Particle Swarm Optimization for extreme learning machine(PSO-ELM) is proposed. Relying on the ELM neural network’s fast learning ability, which can complete the endpoint detection in moment time and output prediction results. This algorithm optimizes the network’s connection structure to a certain extent, but there are still some defects.(2)In order to optimize the connection parameters of ELM neural network better, an algorithm called adaptive fruit fly optimizes ELM(FOAMR-ELM) is put forward. And the algorithm is applied to the speech endpoint identification model.A large number of simulation experiments are done with the help of Matlab software. Through the result of experiments, we can draw the positive conclusion that the ELM endpoint detection model has the best rapidity and high accuracy; and the accuracy has been improved from the PSO-ELM endpoint detection model, but the training time is the longest; eventually, the endpoint detection model based on the FOAMR-ELM has the highest accuracy, at the same time, it has a very good rapidity, which achieves the requirements of practical application.