Speech detection method, training method of prediction model, device, equipment and medium

Abstract

A voice detection method, a training method of a prediction model, a device, equipment and a medium, belonging to the technical field of voice interaction. A multi-modal speech end point detection method, which uses the model to recognize the captured face image, thereby predicting whether the user has the intention to continue speaking, combined with the prediction results, to determine whether the collected audio signal is the speech end point , because on the basis of the acoustic features, the features of the visual modality of the face image are also integrated for detection, even when the background noise is strong or the user pauses during speech, the face image can be used to accurately judge Whether the voice signal is the end point of the voice, thus avoiding the interference caused by background noise and speech pauses, thereby avoiding the problem of too late or premature detection of the end of the voice interaction caused by the interference of background noise and speech pauses, and improving the Accuracy in detecting speech end points.

Description

technical field [0001] The present application relates to the field of voice interaction technology, and in particular to a voice detection method, a prediction model training method, a device, a device, and a medium. Background technique [0002] In the voice interaction technology, in order to realize the human-computer interaction function based on voice, the voice start point and the voice end point in a piece of voice are usually recognized, and the part between the voice start point and the voice end point is intercepted as a voice command. Instructions to instruct the device to perform corresponding operations. Among them, the voice start point is usually triggered by the user's active operation, which is easily determined by the collection time point of the wake-up word, the time point when the voice interaction activation option is triggered, and the voice end point needs to be determined by the device through voice analysis. processing can be obtained. It can be ...

AI Technical Summary

Problems solved by technology

[0004] When the above method is used to detect the end point of the speech, once the background noise is strong, the tail silence duration of the detected audio signal will be longer than the accurate tail silence duration, resulting in the speech end point being easily missed, which will lead to too late Detect that the voice interaction has ended; in addition, once the user pauses during speaking, the tail silence duration of the detected audio signal is smaller than the accurate tail silence duration, which will cause the voice interaction to be detected prematurely in final state

It can be seen that the accuracy of the speech end point detected by this method is poor

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