Audio editing system and audio editing method

Abstract

The invention relates to an audio editing system. The audio editing system comprises a plurality of initial segmentation devices, a multi-sound track fusion device, an audio clustering device and a re-segmentation device, wherein the plurality of the initial segmentation devices are respectively used for initially segmenting audio streams from a plurality of sound tracks into a plurality of different paragraphs; the multi-sound track fusion device is used for integrating segmentation points of the plurality of the initial segmentation devices, selecting the audio stream of the optimal sound track between every two adjacent segmentation points, further getting a plurality of initially segmented fragments and fusing the plurality of the obtained initially segmented fragments into an uniform audio data file; the audio clustering device is used for performing clustering on the plurality of the initially segmented fragments under supervision based on a hierarchical clustering algorithm and clustering the initially segmented fragments belonging to the same nature to a category; and the re-segmentation device is used for training according to the clustering result of the audio clustering device to get a hidden Markov model corresponding to each type and performing Viterbi alignment segmentation on the uniform audio file to get the audio stream after re-segmentation. The accuracy in final speaker clustering can be improved through a high-precision speaker segmentation system.

Description

technical field [0001] The invention relates to the technical field of audio clustering, in particular to an audio editing system and an audio editing method. Background technique [0002] Speaker clustering is a specific application of clustering technology in speech signal processing. Its purpose is to classify speech segments so that each class only contains the same speaker data, and the data of the same speaker are merged into in the same class to obtain speaker-specific information. From an application point of view, speaker clustering technology can be applied to audio information management, retrieval and other fields. It facilitates speaker tracking in audio streams of conferences, voicemails, lectures, and news broadcasts, enabling structured analysis, understanding, and management of audio data. In particular, clustering algorithms are also of great practical value to speech recognition systems. Almost all automatic speech recognition systems today use adaptive ...

AI Technical Summary

Problems solved by technology

These two frameworks have their own advantages and disadvantages. The former inherits the error of the segmentation step without correction in the clustering process. Due to the limitations of the distance measurement method, errors will accumulate; Markov model, the initialization of the model is carried out by directly dividing the audio data into equal parts, the initial error introduced is relatively large, which brings certain problems to the convergence speed of the model, and because the hidden Markov model is based on the characteristics of frame classification , there will be a certain amount of error introduced without restrictions when performing segmentation. The general approach is to add a certain duration limit to the dwell time of each hidden Markov model. This approach brings a lot of flexibility to the system. big limitations

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