Speaker Diarization Multi-Stage Clustering for Long Audio
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Solution Overview
Problem
Existing speaker diarization systems face computational inefficiencies and inaccuracies when dealing with long-form audio due to the high computational cost of spectral clustering and the need for manual annotation of speaker turns, especially in environments with multiple speakers.
Innovation Solution
A multi-stage clustering approach that includes pre-clustering speaker segments into a target number of pre-clusters, followed by spectral clustering on centroid values, reducing computational cost and eliminating the need for manual timestamp annotation by using a speech recognition model to detect speaker turns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral clustering is performed on all speaker segments directly, then speaker diarization accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the speaker segments into two groups: those with high confidence speaker turn tokens and those with low confidence tokens. This segmentation allows the system to apply different clustering strategies to different segments, reducing overall computational complexity while maintaining accuracy for the most important segments.
Solution Approach 2:
The patent extracts and processes only the speaker segments with low confidence speaker turn tokens through spectral clustering, while using the high confidence segments for direct annotation. This extraction approach reduces the computational burden by focusing clustering operations only on the segments that require them most.
2Measurement precision
If manual annotation of speaker turns is used, then speaker diarization accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements a self-service approach where the speech recognition model automatically generates speaker turn tokens and confidence values, eliminating the need for manual annotation. The system uses its own outputs (speaker turn tokens with confidence scores) to guide the diarization process, achieving both automation and maintained accuracy.
Solution Approach 2:
The patent incorporates feedback through confidence values associated with speaker turn tokens. The system uses these confidence values to determine which segments require spectral clustering and which can be directly annotated, creating a feedback loop that optimizes the diarization process based on the speech recognition model's own performance metrics.
3Reliability
If all speaker segments are processed through spectral clustering, then diarization quality is improved, but computational cost increases
Solution Approach 1:
The patent applies local quality by treating different speaker segments differently based on their confidence values. High confidence segments are processed with simpler operations while low confidence segments receive spectral clustering treatment. This localized approach ensures quality where needed while reducing overall computational cost.
Solution Approach 2:
The patent applies partial action by performing spectral clustering only on a subset of speaker segments (those with low confidence tokens) rather than all segments. This partial application of the computationally intensive clustering algorithm maintains diarization quality for critical segments while significantly reducing total computational cost.
Data Source
AI summary
A method (500) includes receiving an input audio signal (122) that corresponds to utterances (120) spoken by multiple speakers. The method also includes processing the input audio to generate a transcription (200) of the utterances and a sequence of speaker turn tokens (224) each indicating a location of a respective speaker turn. The method also includes segmenting the input audio signal into a plurality of speaker segments (225) based on the sequence of speaker tokens. The method also includes extracting a speaker-discriminative embedding from each speaker segment and performing spectral clustering on the speaker-discriminative embeddings to cluster the plurality of speaker segments into k classes. The method also includes assigning a respective speaker label (250) to each speaker segment clustered into the respective class that is different than the respective speaker label assigned to the speaker segments clustered into each other class of the k classes.