Single-Channel Speech Role Separation via Iterative Re-Clustering
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Solution Overview
Problem
The accuracy of unsupervised role separation based on single-channel speech in conference systems is relatively low, particularly in determining the number of roles and their speaking times without prior role information.
Innovation Solution
A method involving segmenting audio signals into segments, performing clustering based on feature information, determining cluster centers, and iteratively refining these centers to improve role separation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised role separation is performed on single-channel speech, then the system can identify roles without prior information, but the accuracy of role separation is relatively low
Solution Approach 1:
The audio signal is divided into multiple audio segments, which are then processed through clustering algorithms. This segmentation allows the system to handle complex single-channel speech by breaking it down into manageable parts, improving both adaptability and accuracy simultaneously
Solution Approach 2:
The patent transforms the problem from traditional single-channel processing by introducing multiple processing dimensions through clustering algorithms that analyze audio segments from different feature perspectives, thereby improving accuracy while maintaining the single-channel input constraint
2Measurement precision
If clustering processing is performed on audio segments, then role identification can be achieved, but errors in cluster assignments occur
Solution Approach 1:
The patent implements iterative optimization where clustering results are evaluated and fed back to refine cluster centers. This feedback mechanism continuously improves cluster assignment accuracy, reducing errors while maintaining high role identification precision
Solution Approach 2:
The system performs preliminary clustering to identify initial cluster centers before final role assignment. This preliminary action allows for error correction and refinement, ensuring both high precision and reliability in the final cluster assignments
Data Source
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AI summary
The present disclosure relates to an audio signal processing method and apparatus, a device and a storage medium. The present disclosure performs a segmenting processing on an audio signal to obtain multiple audio segments, performs a clustering processing on the multiple audio segments according to feature information of each audio segment in the multiple audio segments to obtain one or more first sets, determines a first cluster center of each first set according to the feature information of the audio segment included in each first set, and performs a clustering processing on the multiple audio segments according to the first cluster center of each first set to obtain one or more second sets, where audio segments in a same second set corresponding to a same role label. That is to say, after an initial clustering processing is performed on the multiple audio segments, a re-clustering processing can also be performed on the multiple audio segments according to the first cluster center of each first set, thereby improving an accuracy of an unsupervised role separation based on a single channel speech.