Voice endpoint detection method for emotion recognition, electronic equipment and storage medium

Abstract

The invention relates to voice semantics, and provides a voice endpoint detection method for emotion recognition. The method comprises: acquiring an audio signal; performing processing operation on the audio signal, comprising adding pure noise sections and human voice noise sections in various scenes to the audio signal and randomly setting a signal-to-noise ratio; extracting an MFCC feature anda second-order difference feature of the processed audio signal; inputting the features into a neural network model, and extracting high-dimensional information of the audio signal and front and backassociated features of the audio signal; inputting the extracted high-dimensional information and the associated features of the audio signal into a full-connection network model to obtain a detectionresult of each frame of the audio signal, the detection result comprising human voice and non-human voice; and segmenting the audio signal into a human voice part and a non-human voice part accordingto the detection result of the audio signal. The invention further provides electronic equipment and a storage medium. Accurate voice endpoint detection can be realized in a low signal-to-noise ratioenvironment and a non-stationary environment.

Description

technical field [0001] The invention relates to the technical field of speech semantics, and more specifically, to a speech endpoint detection method for emotion recognition, electronic equipment and a storage medium. Background technique [0002] Before performing speech emotion recognition, it is necessary to accurately identify the endpoint position of the human voice in a long audio, so as to separate the environmental noise from the speaking voice. This technology is voice endpoint detection (VAD), which is a driving force. Speech signal processing technology. Studies have shown that if the start and end positions of speakers can be accurately identified and segmented, the accuracy of subsequent speech tasks can be effectively improved. The traditional VAD technology mainly detects based on the short-term energy, zero-crossing rate, cepstrum feature or entropy of the audio. These methods are simple in principle and have a small amount of calculation. The recognition e...

AI Technical Summary

Problems solved by technology

The traditional VAD technology mainly detects based on the short-term energy, zero-crossing rate, cepstrum feature or entropy of the audio. These methods are simple in principle and have a small amount of calculation. The recognition effect is relatively poor; VAD technology is also used in some speech coding such as G.729B and AMR-NB. The effect is not ideal

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