Stochastic Future Context Training for Speech Processing Neural Networks
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Solution Overview
Problem
Existing speech processing technologies face challenges in balancing the quality and accuracy of speech processing with the delay in providing results, particularly in transitioning between streaming and full context modes.
Innovation Solution
A computer-implemented method and system that trains a neural network using different amounts of future context by sampling a probability distribution, allowing the model to perform well across various context settings, including streaming, full context, and limited future context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If speech processing operates in streaming mode to reduce delay, then the delay in providing results is reduced, but the quality or accuracy of speech processing deteriorates
Solution Approach 1:
The patent applies dynamics by making the future context size adjustable and variable during training. The system dynamically selects different future context sizes from a probability distribution during training, enabling the model to adapt to different delay-quality tradeoff scenarios. In deployment, the context size can be dynamically adjusted based on application requirements.
Solution Approach 2:
The patent changes the parameter of future context size used during speech processing. By training with multiple future context sizes sampled from a probability distribution, the model learns to process speech with varying degrees of future information available, allowing it to maintain accuracy across different delay scenarios.
2Manufacturing precision
If speech processing operates in full context or batch mode to increase quality or accuracy, then the quality or accuracy of speech processing is improved, but the delay in providing results increases
Solution Approach 1:
The system dynamically adjusts the future context size during training by sampling from a probability distribution. This allows the model to learn processing patterns for varying context lengths, enabling it to operate effectively in full context mode when high accuracy is prioritized, while also adapting to streaming mode when low delay is critical.
Solution Approach 2:
The patent creates a universal speech processing model that can function effectively across multiple operating modes (streaming, full context, and limited future context) by training with variable future context sizes. A single model architecture serves multiple functions and deployment scenarios without requiring separate models for each mode.
3Manufacturing precision
If separate models are trained for different context settings to maintain accuracy, then the quality of speech processing is maintained, but the device complexity increases
Solution Approach 1:
The patent merges multiple context-processing capabilities into a single neural network model. By training one model with variable future context sizes sampled from a probability distribution, the system combines what would otherwise require multiple separate models into one unified model, reducing complexity while maintaining accuracy across different context settings.
Solution Approach 2:
The patent develops a universal speech processing model that handles multiple context settings (streaming, full context, limited future context) within a single model architecture. This multi-functional model eliminates the need to train and manage separate models for different deployment scenarios, significantly reducing system complexity.
Data Source
AI summary
The amount of future context used in a speech processing application allows for tradeoffs between performance and the delay in providing results to users. Existing speech processing applications may be trained with a specified future context size and perform poorly when used in production with a different future context size. A speech processing application trained using a stochastic future context allows a trained neural network to be used in production with different amounts of future context. During an update step in training, a future-context size may be sampled from a probability distribution, used to mask a neural network, and compute an output of the masked neural network. The output may then be used to compute a loss value and update parameters of the neural network. The trained neural network may then be used in production with different amounts of future context to provide greater flexibility for production speech processing applications.