Speech Neural Network Context Window Adaptation for Latency
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Solution Overview
Problem
Conventional speech processing systems face inefficiencies due to the need to switch between multiple neural networks to balance latency and accuracy, leading to increased processing costs and reduced performance.
Innovation Solution
A dynamic neural network process that adjusts contextual windows based on processing load, allowing a single neural network to adapt to different accuracy-latency tradeoffs by dynamically changing chunk sizes and context periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple neural networks are used to balance latency and accuracy, then accuracy and latency requirements are met, but processing efficiency deteriorates due to training and switching overhead
Solution Approach 1:
The patent merges multiple specialized neural networks into a single unified neural network that can dynamically adjust its processing behavior. Instead of maintaining separate networks for different accuracy-latency requirements, the invention combines them into one network that adapts its context window size and processing depth based on real-time conditions, eliminating the need for switching between multiple networks and their associated training and coordination overhead.
Solution Approach 2:
The patent introduces dynamic adaptability into the neural network by enabling it to adjust its context window size and processing parameters in real-time based on processing load and performance requirements. This dynamic behavior allows a single network to exhibit characteristics of multiple specialized networks, adapting its effective complexity and processing depth to meet varying accuracy and latency requirements without requiring multiple fixed-configuration networks.
2Measurement precision
If attention mechanisms are fully utilized in neural networks, then processing accuracy is improved, but latency increases significantly
Solution Approach 1:
The patent applies dynamics by making the context window size adjustable rather than fixed. The neural network can dynamically reduce its context window size when low latency is required, processing fewer tokens and reducing attention computation time. Conversely, when accuracy is prioritized and latency constraints are relaxed, the network expands its context window to process more tokens with full attention, thereby adapting the accuracy-latency tradeoff in real-time based on system conditions.
3Productivity
If a single neural network is used instead of multiple networks, then processing efficiency is improved, but adaptability to different accuracy-latency requirements deteriorates
Solution Approach 1:
The patent employs parameter changes by adjusting the context window size as a controllable parameter of the neural network. This parameter can be modified dynamically based on processing load, accuracy requirements, and latency constraints. By changing this key parameter, the single neural network can adapt its behavior to meet different accuracy-latency requirements, effectively providing the versatility of multiple specialized networks while maintaining the processing efficiency of a unified architecture.
Data Source
AI summary
A method, computer program product, and computing system for dividing a speech signal into a plurality of chunks. A context window is defined for processing a chunk of the plurality of chunks using a neural network of a speech processing system. A processing load associated with the speech processing system is determined. The context window is dynamically adjusted based upon, at least in part, the processing load associated with the speech processing system.