RNN Reset Parameters via Statistical Analysis for ASR Stability
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Solution Overview
Problem
Automatic speech recognition (ASR) systems using recurrent neural networks (RNNs) face instability issues over time, leading to decreased recognition accuracy, which existing methods address inadequately through pre-defined reset intervals and training data quantities that may not be optimized for varying acoustic models or environments.
Innovation Solution
The implementation of analysis-based calculation of reset parameters for RNNs, including the determination of optimal reset time and context duration, based on statistical analysis of RNN performance, to improve stability and performance across different acoustic models and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-defined execution time interval is used for RNN reset, then the system operation is simple, but recognition accuracy decreases if the interval is too long and computational efficiency is impacted if the interval is too short
Solution Approach 1:
The patent transforms the static pre-defined reset interval into a dynamic adaptive interval. The system monitors RNN stability metrics during operation and automatically adjusts the reset timing based on actual network state. This allows the reset interval to extend as long as stability is maintained, maximizing computational efficiency while preventing accuracy degradation through data-driven adaptation rather than fixed scheduling.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor RNN output stability and use this information to determine optimal reset moments. By measuring metrics such as output variance or divergence over time, the system provides feedback about network stability to the reset control logic, enabling accuracy-preserving resets that occur only when necessary, thus avoiding unnecessary computational overhead.
2Reliability
If a pre-defined quantity of training data is used for reset, then the system operation is simple, but adequate context may not be provided resulting in insufficient reset performance or excessive data resulting in slower resets
Solution Approach 1:
The patent replaces the static pre-defined training data quantity with a dynamic adaptive selection mechanism. The system evaluates the actual context needs of the RNN by analyzing stability metrics and adjusts the amount of training data used for reset accordingly. This ensures that sufficient context is provided for effective reset without unnecessarily loading excessive data, optimizing both reset performance and speed.
Solution Approach 2:
The patent applies partial action by using only the necessary portion of available training data for the reset process. Rather than always using a fixed large dataset, the system determines the minimum adequate context length needed based on monitored stability characteristics, thereby achieving effective reset with reduced computational burden and faster execution time.
Data Source
AI summary
Techniques are provided for calculating reset parameters for recurrent neural networks (RNN). A methodology implementing the techniques according to an embodiment includes generating a sequence of statistics. The calculation of each statistic is based on outputs of an RNN that is periodically re-initialized at a selected RNN reset time such that each of the calculated statistics is associated with a unique RNN reset time selected from a pre-determined range of reset times. The method further includes analyzing the sequence to identify a maximum interval during which the sequence remains relatively constant. The method further includes selecting a reset time parameter and reset context duration parameter, for re-initialization of the RNN during operation. The reset time parameter is based on the duration of the identified maximum interval and the reset context duration parameter is based on a time associated with the starting point of the identified maximum interval.