A voice classification method, device, server and storage medium

Abstract

The embodiment of the present invention discloses a voice classification method, device, server, and storage medium, wherein the voice classification method includes: using the Mel-frequency cepstral coefficient MFCC algorithm to obtain the MFCC feature matrix of the target short voice, and converting the MFCC feature matrix is the target image; based on the deep learning model, the target image feature of the target image is extracted; the target image feature is input into a pre-trained voice classifier, and the category of the target short voice is output. The embodiment of the present invention overcomes the disadvantage that the existing voice classification method ignores the deep information of the voice content and can only roughly evaluate the voice with a large difference in content, and achieves the effect of quickly and effectively classifying the voice data.

Description

technical field

[0001] The embodiment of the present invention relates to the application field of Internet technology, and in particular to a voice classification method, device, server and storage medium. Background technique

[0002] With the rapid development of the Internet industry and the expansion of voice information, how to quickly and accurately classify voice data in massive information and save computing resources is a difficult point at present.

[0003] The existing speech classification method usually calculates the MFCC features of each frame in the speech data, and then stitches the MFCC features of each frame into the overall features of short speech, trains a classifier and performs feature classification, and then obtains classification labels. However, based on the general speech classification method, the deep information of the speech content is ignored, and only a rough assessment can be made on the speech with a large difference in content. Conten...

Images