Incremental Utterance Decoder Selection for Speech Recognition
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Solution Overview
Problem
Offline speech recognition systems face significant resource costs in computation, storage, and time when processing spoken utterances through multiple decoders to reduce recognition error rates, as using all available decoders results in high resource usage with marginal accuracy gains.
Innovation Solution
An incremental speech recognition system that selectively engages additional utterance decoders based on their accuracy, performance, and diversity, stopping when a specified reliability threshold is met, thereby minimizing resource usage while achieving accuracy approaching the maximum obtainable by combining all decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple utterance decoders are used to process spoken utterances, then recognition error rate is reduced, but resource cost (computation power, storage, time) increases significantly
Solution Approach 1:
The system applies partial action by selectively engaging additional utterance decoders only when the confidence score of the initial decoder falls below a threshold. Instead of always using all available decoders, the system uses just enough decoders to achieve the desired recognition accuracy, thereby reducing resource consumption while maintaining reliability.
Solution Approach 2:
The system dynamically adjusts the number of decoders engaged based on the confidence score of the initial decoding result. When confidence is high, only the initial decoder is used. When confidence is low, additional decoders are engaged. This dynamic approach optimizes the balance between recognition accuracy and resource usage.
2Measurement precision
If all available offline utterance decoders are used, then recognition accuracy is maximized, but computation power and processing time requirements become significant
Solution Approach 1:
The system uses partial action by engaging additional decoders only when necessary (when confidence score is below threshold). This avoids the excessive computation power required to run all decoders regardless of need, while still achieving near-maximum accuracy by engaging enough decoders to resolve uncertain cases.
Solution Approach 2:
The system employs a tiered approach where a single initial decoder provides the first line of defense for most cases. Only when this 'cheap' initial decoder cannot provide sufficient confidence does the system engage additional 'expensive' decoders. This strategy achieves high accuracy while minimizing the use of high-computation resources.
3Reliability
If additional utterance decoders are engaged, then decoded utterance accuracy is improved, but resource usage increases
Solution Approach 1:
The system applies partial action by engaging additional decoders only for a portion of utterances that fall below the confidence threshold. For utterances with high confidence, the initial decoder suffices. This selective approach improves accuracy where needed while avoiding unnecessary resource usage for clear cases.
Solution Approach 2:
The system uses feedback from the confidence score of the initial decoder to determine whether additional decoders are needed. This feedback mechanism ensures that resources are allocated efficiently - additional computational resources are consumed only when the initial decoding result indicates uncertainty, thereby optimizing the trade-off between accuracy and resource usage.
Data Source
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AI summary
An incremental speech recognition system. The incremental speech recognition system incrementally decodes a spoken utterance using an additional utterance decoder only when the additional utterance decoder is likely to add significant benefit to the combined result. The available utterance decoders are ordered in a series based on accuracy, performance, diversity, and other factors. A recognition management engine coordinates decoding of the spoken utterance by the series of utterance decoders, combines the decoded utterances, and determines whether additional processing is likely to significantly improve the recognition result. If so, the recognition management engine engages the next utterance decoder and the cycle continues. If the accuracy cannot be significantly improved, the result is accepted and decoding stops. Accordingly, a decoded utterance with accuracy approaching the maximum for the series is obtained without decoding the spoken utterance using all utterance decoders in the series, thereby minimizing resource usage.