Latent Speaker Bottleneck for Overlapping Speech Diarization
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Solution Overview
Problem
Existing speaker diarization systems struggle with accurately segmenting speech from multiple speakers due to unsuitable speaker-discriminative embeddings and unsupervised clustering methods that fail to disambiguate speakers in overlapping conversations, leading to inaccurate speech recognition.
Innovation Solution
A speaker diarization model incorporating a diarization encoder, a latent speaker bottleneck module (LSBM), and a diarization decoder, which generates embeddings and selects subsets based on audio features to predict voice activity indicators, trained using unlabeled and labeled data to optimize speaker identification in overlapping speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsupervised clustering methods are used for speaker diarization, then the system can operate without labeled data, but the accuracy of speaker segmentation deteriorates in overlapping conversations
Solution Approach 1:
The patent introduces speaker-discriminative embeddings as an intermediary representation that captures speaker-specific characteristics. These embeddings serve as a bridge between the audio input and the clustering process, enabling the system to distinguish speakers in overlapping conversations even without labeled training data. The embeddings are generated using pre-trained speaker verification models, which provide robust speaker discrimination capabilities that standard clustering methods lack.
Solution Approach 2:
The patent transforms the speaker diarization problem by changing the parameter space from raw audio features to speaker-discriminative embeddings. This parameter transformation allows the system to operate in a feature space where speaker separation is more effective, particularly for overlapping speech. The embeddings capture invariant speaker characteristics that remain stable across different speaking conditions, thereby improving segmentation accuracy.
2Reliability
If standard speaker-discriminative embeddings are used, then the system can identify speaker characteristics, but the embeddings are unsuitable for accurate speaker segmentation in overlapping speech
Solution Approach 1:
The patent segments the speaker diarization process into distinct functional components: audio feature extraction, speaker-discriminative embedding generation, embedding selection for active speakers, and voice activity detection. This segmentation allows each component to be optimized independently. Specifically, the system generates multiple embeddings per time step and selectively uses only those corresponding to active speakers, thereby avoiding the degradation that occurs when embeddings from inactive or overlapping speakers are included.
Solution Approach 2:
The patent introduces dynamic embedding selection where the system adaptively chooses which embeddings to use at each time step based on voice activity indicators. This dynamic approach contrasts with static methods that use all generated embeddings uniformly. The system dynamically adjusts the embedding set by filtering out embeddings from inactive speakers and emphasizing those from active speakers, thereby maintaining high segmentation accuracy in overlapping speech scenarios.
3Extent of automation
If unsupervised clustering is applied to audio features, then the system can partition speech segments, but it fails to disambiguate speakers in overlapping conversations
Solution Approach 1:
The patent performs preliminary generation of multiple speaker-discriminative embeddings for each time step before the clustering and voice activity detection stages. These pre-generated embeddings are stored and then selectively utilized based on voice activity indicators. This preliminary action ensures that when clustering is performed, the system has access to a rich set of speaker-specific representations, and can choose the most appropriate ones for active speakers, thereby improving disambiguation accuracy in overlapping speech.
Data Source
AI summary
A method includes receiving audio data characterizing a conversation between two or more speakers. The method also includes generating a sequence of audio features based on the audio data. For each output step of a plurality of output steps, the method includes generating a corresponding set of embeddings for the corresponding audio features, selecting a subset of the embeddings for the corresponding output step from the corresponding set of embeddings, and predicting a respective voice activity indicator for each respective speaker of the two or more speakers based on the subset of the embeddings selected for corresponding output step. The respective voice activity indicator indicates whether a voice of the respective speaker is active or inactive at the corresponding output step.