Dynamic Emission Control in RNN-T Speech Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
RNN-T models face challenges in reducing inference complexity while maintaining accuracy, especially with limited GPU resources and varying sequence lengths, leading to increased computational cost and degradation in accuracy.
Innovation Solution
Implementing a dynamic adjustment of the number of emissions per frame in speech processing systems operating with large stride values, allowing for optimal trade-offs between accuracy and speed by dynamically setting the stride value based on data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a large stride factor is used in the encoder to reduce decoding cost, then computational complexity is reduced, but accuracy worsens sharply due to static emission restrictions
Solution Approach 1:
The patent applies dynamics by transitioning from a static emission restriction (monotonic RNN-T allowing only one emission per frame) to a dynamic emission control mechanism. The system dynamically adjusts the number of emissions per frame based on the stride factor and data characteristics, allowing flexible adaptation between computational efficiency and accuracy. This resolves the contradiction by enabling large stride factors without the rigid one-emission-per-frame constraint that previously caused accuracy degradation.
Solution Approach 2:
The patent changes the parameter of emissions per frame from a fixed value (1 in monotonic RNN-T) to a dynamically adjustable parameter. By modifying the emission parameter based on stride factor and input characteristics, the system can optimize the trade-off between computational complexity and accuracy. This parameter change enables the system to maintain high accuracy even with large stride factors that reduce computational complexity.
2Speed
If the number of emissions per frame is limited to one (monotonic RNN-T), then decoding speed improves, but outputs are omitted leading to deletion errors
Solution Approach 1:
The system dynamically adjusts the emission limit per frame based on the stride factor and input characteristics. When the stride factor is large, the system allows more than one emission per frame to prevent output omission, while still maintaining faster decoding compared to processing every single token. This dynamic adjustment resolves the contradiction between decoding speed and transcription reliability.
Solution Approach 2:
The emission parameter is changed from a fixed value of 1 to a variable parameter that adapts to different stride factors and input characteristics. This parameter change enables the system to maintain high decoding speed while preventing deletion errors by allowing multiple emissions when necessary, thus improving transcription reliability without sacrificing speed.
3Adaptability or versatility
If GPU resources are limited and CPU inference is used, then system accessibility improves, but processing efficiency decreases
Solution Approach 1:
The patent optimizes the emission parameter to work efficiently with large stride factors, which significantly reduces the encoder output length and thereby decreases the computational burden on CPU resources. This parameter optimization enables RNN-T models to achieve high processing efficiency on CPUs, making the system accessible even when GPU resources are limited, thus resolving the contradiction between accessibility and efficiency.
4Device complexity
If a static emission restriction is applied, then device complexity is reduced, but accuracy deteriorates due to omitted outputs
Solution Approach 1:
The patent replaces the static emission restriction with a dynamic emission control mechanism that adapts to the stride factor and input characteristics. This dynamic approach maintains relatively simple device complexity while significantly improving transcription accuracy by allowing flexible emission adjustments, thus resolving the contradiction between complexity and precision.
Data Source
AI summary
A method, computer program product, and computing system for dynamically adjusting the number of emitted tokens per frame in speech processing systems operating with large stride values. The number of emitted tokens per frame can be dynamically adjusted in speech processing systems operating with large stride values by processing a signal frame according to a time-synchronous beam search technique at a frame rate based on a stride value; determining a hypothesis score for each hypothesis of a set of first information for the signal frame; determining a hypothesis score for each hypothesis of a set of second information for the signal frame; comparing a worst hypothesis score of the set of first information to a sum of a best hypothesis score of the set of second information and a threshold value; and ceasing processing of the signal frame when the worst hypothesis score of the set of first information is greater than the sum of the best hypothesis score of the set of second information and the threshold value.