End point detection method and system of voice identification system

Abstract

The present invention provides an end point detection method and system of a voice identification system, wherein the method comprises the steps of training an acoustics identification model based on a long / short term memory neural network; primarily identifying a voice end point of a to-be-identified voice signal by a preset voice end point detection algorithm; extracting the voice characteristic information of the to-be-identified voice signal frame by frame, and inputting the voice characteristic information in the acoustics identification model to enable the acoustics identification model to generate an acoustics identification result of the to-be-identified voice signal according to the voice characteristic information; adjusting the primarily identified voice end point according to the acoustics identification result. The end point detection method of the voice identification system of the embodiment of the present invention provides an end point detection mode of adjusting the primarily identified voice end point by the acoustics identification result, thereby positioning the voice end point of the to-be-identified voice signal accurately, improving the voice end point detection accuracy, and further being able to improve the voice identification accuracy, and improving the performance of the voice identification system.

Description

technical field [0001] The invention relates to the technical field of speech recognition, in particular to an endpoint detection method and system of a speech recognition system. Background technique [0002] With the development of human-computer information interaction technology, speech recognition technology shows its importance. In speech recognition system, speech endpoint detection is one of the key technologies in speech recognition. Speech endpoint detection refers to finding the starting point and ending point of the speech part in the continuous sound signal. Whether the endpoint detection is accurate or not will directly affect the performance of the speech recognition system. Specifically, the detection results of speech endpoint detection have a great impact on the recognition effect of the back-end acoustic model, decoder, and language model. If the endpoint is segmented and stored incorrectly, it will lead to missed recognition or misidentification. , whi...

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Problems solved by technology

[0004] However, in the process of realizing the present invention, the inventors have found that the above-mentioned speech endpoint detection method has the following problems: (1) the above-mentioned speech endpoint detection method is suitable for stationary noise and high SNR environment, but it is not suitable for non-stationary noise and relatively high signal-to-noise ratio. In a low SNR environment, the detection effect of the above-mentioned speech endpoint detection method is not good, and the accuracy of the detected speech endpoint is low; (2) for speech signals under different SNRs, it is difficult to select a suitable threshold, and it is impossible to Ensure detection accuracy in quiet environment and detection accuracy in noisy environment

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