Speaker Diarization via Early-Stop Clustering and High-Similarity Selection
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Solution Overview
Problem
Traditional speaker diarization methods often result in impure clusters due to missed speaker change points, leading to inaccurate frame alignment and reduced accuracy in identifying speakers in audio streams with multiple speakers.
Innovation Solution
The implementation of early-stop clustering, which generates more clusters than the number of speakers, allowing for the selection of high-similarity clusters for establishing a speaker classification model, and the use of speaker bottleneck features for better representation and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speaker diarization methods are used to cluster speech segments, then the clustering process is simple, but the clusters become impure due to missed speaker change points, leading to reduced accuracy
Solution Approach 1:
The audio stream is segmented into speech segments based on speaker change points, and then these segments are clustered into groups. The segmentation step divides the complex task of speaker identification into manageable units, allowing for more accurate clustering that reduces impurity while maintaining simplicity in the overall process
Solution Approach 2:
Speaker change points are detected in advance before clustering occurs. This preliminary detection of change points allows the clustering process to start with pre-organized speech segments, ensuring that clusters are formed from homogeneous data and preventing impurity from the outset
2Manufacturing precision
If the number of clusters is set equal to the number of speakers, then the model is simple, but missed speaker change points result in inaccurate frame alignment
Solution Approach 1:
The clustering process generates more clusters than the actual number of speakers (excessive action). This allows the system to capture subtle speaker changes and variations that would be lost with exact matching, enabling more precise frame alignment while the complexity is managed through subsequent selection of the most representative clusters
3Measurement precision
If more clusters are generated than the number of speakers, then cluster selection becomes necessary increasing complexity, but this enables selection of high-similarity clusters for better speaker representation
Solution Approach 1:
From the set of generated clusters, the method extracts and selects only the high-similarity clusters that best represent the speakers. This extraction process filters out impure or redundant clusters, retaining only the most representative ones for final speaker identification, thereby improving accuracy while managing complexity through selective retention
Data Source
AI summary
A method and apparatus for speaker diarization with early-stop clustering, segmenting an audio stream into at least one speech segment (710), the audio stream comprising speeches from at least one speaker; clustering the at least one speech segment into a plurality of clusters (720), the number of the plurality of clusters being greater than the number of the at least one speaker; selecting, from the plurality of clusters, at least one cluster of the highest similarity (730), the number of the selected at least one cluster being equal to the number of the at least one speaker; establishing a speaker classification model based on the selected at least one cluster (740); and aligning, through the speaker classification model, speech frames in the audio stream to the at least one speaker (750).