Speaker Diarization Using Embedding Pruning for Accurate Enumeration
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Solution Overview
Problem
Diarization systems face inaccuracies in estimating the number of speakers due to variations in speech patterns, accents, languages, speaking rates, background noise, and overlapping speech, leading to errors in speaker labeling and transcription.
Innovation Solution
Implementing embedding pruning to selectively remove audio content segments that could contribute to misestimation of the number of speakers, using spectral clustering to accurately identify the number of speakers based on remaining embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diarization systems process all audio content segments, then comprehensive speaker coverage is achieved, but measurement precision of speaker number estimation deteriorates due to noise and variations
Solution Approach 1:
The patent extracts and removes harmful audio segments from the processing pipeline through embedding pruning. By identifying and eliminating segments with low speech quality, high background noise, or overlapping speech characteristics, the system prevents these harmful factors from degrading the speaker number estimation accuracy in subsequent clustering operations.
Solution Approach 2:
The patent applies preliminary filtering through embedding pruning before the main speaker estimation task. By pre-processing audio segments to remove problematic content and retaining only high-quality segments, the system prepares cleaner input data for the spectral clustering algorithm, thereby improving final measurement precision.
2Measurement precision
If embedding pruning is applied to remove problematic audio segments, then speaker estimation accuracy is improved, but information loss occurs from excluded segments
Solution Approach 1:
The patent changes the parameter of segment inclusion by introducing a quality threshold based on embedding characteristics. Segments are transformed from a binary included/excluded state to a quality-weighted state, where only segments meeting minimum quality criteria are retained for speaker estimation, balancing information retention with accuracy improvement.
3Measurement precision
If spectral clustering is used on pruned embeddings, then speaker number estimation accuracy is enhanced, but processing complexity increases
Solution Approach 1:
The patent segments the processing pipeline into distinct functional stages: audio segmentation, embedding generation, quality-based pruning, and spectral clustering. This segmentation allows each component to be optimized independently and facilitates parallel processing of multiple audio segments, managing complexity through modular architecture.
Data Source
AI summary
Techniques for diarization using embedding pruning are disclosed. A set of audio content segments and their associated tokens are accessed by a speaker enumeration module of a speech processing engine. The speaker enumeration module uses various pruning criteria to prune audio content segments from the set to result in a pruned set of audio content segments. The pruned set of audio content segments is analyzed using a clustering process to determine a number of speakers. The number of speakers is used in a second clustering process to identify speakers in the original set of audio content segments prior to pruning. A transcription of the original audio content with speaker labels is generated using the number of speakers identified for the pruned set of audio content segments.