Low-latency speech separation via neural segmentation
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Solution Overview
Problem
Existing speech separation technologies face challenges in effectively separating target speech from background interference, particularly in dynamic and noisy environments such as vehicles, where multiple speakers and various sources of noise complicate the separation process.
Innovation Solution
A system and method for low-latency single-microphone speech separation using speaker representations, which involves generating a two-dimensional representation of a speech mixture, initial separation, refinement based on speaker representations, estimating masks per speaker, and applying these masks to create per-speaker representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech separation methods are used, then speech separation can be achieved, but latency is high and performance degrades in noisy environments
Solution Approach 1:
The speech separation process is divided into distinct stages: initial separation using a first neural network, followed by refinement using a second neural network. This segmentation allows each network to specialize in specific tasks, improving overall performance while maintaining low latency through efficient processing at each stage.
Solution Approach 2:
The system performs preliminary speech separation using a first neural network before refinement. This preliminary action provides a good initial separation that the second neural network can then refine, reducing the computational burden and latency of the overall process while maintaining high performance.
2Measurement precision
If complex separation algorithms are used to handle multiple speakers and noise, then separation accuracy improves, but computational complexity increases
Solution Approach 1:
The computational task is segmented into two neural networks with different complexities. The first network handles initial separation with moderate complexity, while the second network performs refinement with lower computational requirements. This segmentation achieves high accuracy without requiring a single overly complex system.
Solution Approach 2:
The system applies refinement only where needed - the second neural network processes the initial separation results to correct specific errors and improve accuracy in challenging cases, rather than applying full complex processing to all inputs uniformly.
Data Source
AI summary
A system for speech separation includes data processing hardware and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations including (i) generating a two-dimensional representation of a speech mixture, (ii) separating the speech mixture into an initial separation (iii) supplying the initial separation and speaker representations to a refinement module, (iv) refining the initial separation based on the initial separation and the speaker representations, (v) estimating a mask per speaker, and (vi) applying the masks to the two-dimensional representation to create two-dimensional, per-speaker representations.