End-to-End Iterative Speaker Embeddings for Real-Time Speech Diarization
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Solution Overview
Problem
Existing speaker diarization systems are limited by unsupervised clustering algorithms that fail to leverage labeled training data, struggle with memory constraints in long sequences, and are inefficient for real-time processing, leading to inaccurate speech recognition in multi-speaker environments.
Innovation Solution
An end-to-end neural diarization system (DIVE) that combines a temporal encoder, iterative speaker selector, and voice activity detector to predict speaker embeddings and activity indicators, optimizing feature extraction and clustering for improved diarization accuracy and real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsupervised clustering algorithms are used for speaker diarization, then the system can process audio streams without labeled data, but the diarization accuracy deteriorates due to inability to leverage labeled training data
Solution Approach 1:
The patent replaces traditional unsupervised clustering algorithms (mechanical/systematic approach) with a neural network-based supervised learning system. The neural network is trained on labeled speaker diarization data and learns to predict speaker labels directly, substituting the clustering mechanism with a learned model that achieves superior accuracy by leveraging labeled training data.
2Reliability
If traditional clustering methods are applied to long audio sequences, then complete speaker coverage is achieved, but memory constraints are exceeded leading to processing failures
Solution Approach 1:
The patent segments long audio sequences into smaller overlapping chunks that can be processed independently by the neural network. Each chunk is processed to predict speaker labels, and the results are merged to achieve complete speaker coverage for the entire long audio sequence, thereby reducing memory consumption while maintaining reliability.
3Measurement precision
If complex clustering algorithms are used to improve speaker discrimination, then diarization accuracy improves, but processing speed deteriorates making real-time application difficult
Solution Approach 1:
The patent replaces complex iterative clustering algorithms with a neural network model that performs speaker discrimination in a single forward pass. The neural network learns speaker representations and makes predictions efficiently, achieving high accuracy without the computational burden of traditional clustering methods, thus enabling real-time processing.
4Ease of manufacture
If feature extraction and clustering are performed separately, then modular processing is achieved, but overall system efficiency deteriorates due to multiple processing passes
Solution Approach 1:
The patent merges the feature extraction and clustering operations into a single integrated neural network model. The network simultaneously performs acoustic feature extraction, speaker embedding generation, and speaker label prediction in one end-to-end processing pass, eliminating the need for separate modular steps and improving overall system efficiency.
Data Source
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AI summary
A method (600) includes receiving an audio signal (122) corresponding to utterances (120) spoken by multiple speakers (10). The method also includes encoding the input audio signal into a sequence of T temporal embeddings (220). During each of a plurality of iterations each corresponding to a respective speaker of the multiple speakers, the method includes selecting a respective speaker embedding (240) for the respective speaker by determining a probability that the corresponding temporal embedding includes a presence of voice activity by a single new speaker for which a speaker embedding was not previously selected during a previous iteration and selecting the respective speaker embedding for the respective speaker as the temporal embedding. The method also includes, at each time step, predicting a respective voice activity indicator (262) for each respective speaker of the multiple speakers based on the speaker embeddings selected and the temporal embedding.