Speech Diarization Sorting for Lower-Complexity Multi-Speaker Training
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Solution Overview
Problem
Existing speech diarization techniques face computational inefficiencies and complexity when assigning speech to multiple speakers, particularly with permutation invariant learning methods that require extensive computational resources and struggle with real-time or near real-time processing.
Innovation Solution
Implementing a transformer-based architecture with sorting functionality that sorts speech embeddings based on characteristics like variance or arrival time, reducing the need for permutation invariant training and allowing for more efficient and accurate diarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If permutation invariant learning (PIL) or training (PIT) is used to assign speech to multiple speakers, then the diarization accuracy is improved by considering all speaker permutations, but the computational complexity increases prohibitively with the number of speakers (e.g., 10 speakers create over a million permutations)
Solution Approach 1:
The system performs preliminary sorting of speaker arrays based on arrival time (when each speaker first speaks) before conducting the loss calculation. This preliminary ordering action eliminates the need to evaluate all possible permutations, reducing computational complexity from factorial O(n!) to linear O(n) sorting operations, while still achieving accurate speaker assignment
Solution Approach 2:
Instead of iterating through all possible speaker permutations to find the best match (traditional PIL approach), the invention inverts the problem by imposing a fixed sorting criterion (arrival time) on the ground truth speaker arrays. This inversion transforms the combinatorial optimization problem into a straightforward comparison task, dramatically reducing computational requirements
2Device complexity
If conventional diarization models are trained without sorting functionality, then the model architecture remains simple, but the training becomes computationally expensive and cannot scale to handle varying numbers of speakers efficiently
Solution Approach 1:
The system incorporates dynamic sorting functionality that adapts to varying numbers of speakers in real-time. The sorting mechanism naturally handles any number of speakers by ordering them based on arrival time, allowing the model to scale dynamically without retraining or architectural changes, thus improving training efficiency and productivity
Solution Approach 2:
The invention changes the parameter representation by introducing sorted speaker arrays with arrival time ordering as a new parameter regime. This parameter transformation enables the model to handle variable speaker counts efficiently, as the sorting criterion provides a consistent framework that works regardless of how many speakers are present, improving both training efficiency and scalability
3Adaptability or versatility
If speaker arrays are not sorted during training, then the ground truth labeling can use arbitrary speaker indexing, but it becomes difficult to compare ground truth to network outputs and requires computationally expensive permutation matching
Solution Approach 1:
The system applies preliminary sorting to the ground truth speaker arrays based on arrival time before any comparison or loss calculation is performed. This preliminary ordering establishes a consistent reference framework that eliminates the need for time-consuming permutation matching during training, significantly reducing training time while maintaining the flexibility to handle arbitrary speaker assignments in the input data
Data Source
AI summary
In various examples, multi-speaker audio is diarized using artificial intelligence models including a sorting functionality. Sorting is performed based on the first time a speaker is indicated as speaking and/or based on the variance of a dimension of a speech embedding. Sorting speech sequences has the advantage of requiring fewer computations of cross-entropy loss during training and/or allowing diarization models to focus on the difference between speakers. Diarized speech may be used to create a transcript in conjunction with automatic speech recognition models.