Multi-Scale Speaker Diarization for Overlapping Speech Attribution
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Solution Overview
Problem
Existing speaker diarization systems face challenges in accurately attributing speech segments to the correct speakers, especially with short segments having insufficient speech information and long segments prone to errors due to overlapping speakers or segment boundary issues.
Innovation Solution
A multi-scale approach using dynamically-weighted embeddings, where speech is segmented into multiple intervals, processed by a speaker embeddings model, clustered, and weighted using a dynamic weights model to generate context vectors, followed by a speaker labeling model for accurate speaker identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech is segmented into short intervals for high temporal resolution, then temporal precision is improved, but speaker attribution accuracy deteriorates due to insufficient speech information
Solution Approach 1:
The speech signal is divided into multiple time intervals (e.g., 0.5s, 1.0s, 1.5s) to create segments at different scales. This allows the system to capture both fine temporal details and sufficient speech information for accurate speaker attribution by processing multiple segmentations simultaneously.
Solution Approach 2:
The patent introduces a temporal scale dimension by processing speech at multiple time interval lengths. Instead of using a single segmentation scale, the system creates embeddings across different temporal dimensions (0.5s, 1.0s, 1.5s intervals) and combines them to resolve the contradiction between temporal precision and attribution accuracy.
2Reliability
If speech is segmented into long intervals for sufficient speech information, then speaker attribution accuracy is improved, but temporal resolution deteriorates due to boundary issues and overlapping speakers
Solution Approach 1:
By creating multiple segmentations at different scales (short 0.5s intervals, medium 1.0s intervals, long 1.5s intervals), the system can simultaneously achieve fine temporal resolution and sufficient speech information. Each scale captures different aspects of the speech signal.
Solution Approach 2:
The system processes more segmentations than a single-scale approach would use, creating embeddings at multiple temporal scales. This excessive processing of different interval lengths ensures that both short-term temporal details and long-term speaker characteristics are captured.
3Reliability
If multiple time scales are processed to resolve segmentation issues, then speaker attribution accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent employs a dynamic weights model that adaptively assigns importance weights to embeddings from different time scales. Instead of treating all scales equally or using fixed weights, the system dynamically determines which temporal scales are most relevant for each speech segment, reducing unnecessary computational overhead while maintaining accuracy.
Solution Approach 2:
The system changes the parameter of temporal scale by processing embeddings at multiple interval lengths (0.5s, 1.0s, 1.5s). This parameter variation allows the model to capture speaker characteristics at different temporal resolutions, improving attribution accuracy while the dynamic weighting mechanism manages computational complexity.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker diarization. The techniques include obtaining a speaker embedding for various reference times of a speech and for various differently-sized time intervals, identifying a plurality of clusters, each cluster associated with a different speaker of the speech. The techniques further include computing, using the speaker embeddings, a set of embedding weights for various differently-sized time intervals, and identifying, using the computed set of the embedding weights, one or more speakers speaking at a respective reference time.