Human voice segmentation method and device

Abstract

An embodiment of the invention provides a human voice segmentation method and a human voice segmentation device. The human voice segmentation method comprises the steps of: extracting feature vectorsfrom audio data; performing voice activation monitoring on the audio data, and labeling muted segments and voice segments separately; extracting the voice segments according to labels, segmenting thevoice segments according to a predetermined time length, performing clustering operation on the feature vectors in the segmented voice segments by adopting a probability distribution clustering method, and outputting corresponding clustering labels; and arranging the voice segments corresponding to the different clustering labels according to a time sequence, and outputting the voice segments withdifferent clustering labels after arrangement and merging. The human voice segmentation method adopts the probability distribution clustering method for performing clustering operation, can perform rapid clustering on the feature vectors of voice without modeling the voice segments, adds the voice activation monitoring, only processes the voice segments, improves the working efficiency, and solves the technical problem of low efficiency and long time consumption of the traditional human voice segmentation system.

Description

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AI Technical Summary

Benefits of technology

This technology uses an algorithm called Probability Distribution Clusters (PDCluster) for performing clustered speech processing tasks without requiring any models or assumptions about each section being analyzed. By doing this process faster than current methods, it saves effort by reducing manual labor required while still achieving accurate results.

Problems solved by technology

This patented problem addressed by the present inventors relates to an efficient way to label sound changes between different groups within conversations (Voiceover) based on their spoken words alone rather than relying solely upon previous annotations made manually).

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