Speech Recognition Hypothesis Filtering via Grammar Testing
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Solution Overview
Problem
Training machine learning speech recognition systems for complex alphanumeric character strings is resource-intensive due to the vast number of possible combinations, making it impractical to pass all hypotheses through linguistic analysis, especially when computational resources are limited.
Innovation Solution
A method involving a trained language model, such as a convolutional or recurrent neural network, that generates hypotheses, applies a grammar test to identify and rank failure patterns, and generates replacement hypotheses with scores, reducing the number of outputs for further processing by selecting only high-scoring and frequent hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all hypotheses are passed through linguistic analysis, then speech recognition accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies partial action by performing linguistic analysis selectively on only those hypotheses that fail grammar tests, rather than analyzing all hypotheses. This reduces computational load while maintaining accuracy for problematic cases. The system identifies and focuses resources on hypotheses with grammar failures, applying linguistic analysis only where needed.
Solution Approach 2:
The patent implements preliminary action by performing grammar testing on hypotheses before subjecting them to full linguistic analysis. This preliminary filter identifies hypotheses that are likely to fail, allowing the system to prioritize linguistic analysis resources on cases most likely to benefit from it, thereby improving efficiency while maintaining accuracy.
2Reliability
If a comprehensive training data set covering all alphanumeric combinations is used, then speech recognition reliability is improved, but training time and resource requirements increase
Solution Approach 1:
The patent applies partial action in training by focusing on generating and analyzing only the top-scoring hypotheses rather than exhaustively processing all possible alphanumeric combinations. This selective approach maintains reliability for common speech patterns while dramatically reducing training time and resource requirements.
Solution Approach 2:
The patent changes parameters by using confidence scoring and hypothesis ranking to prioritize which training examples receive full linguistic analysis. By adjusting the threshold for when to apply linguistic analysis, the system can balance between training comprehensiveness and resource consumption, achieving good reliability without exhaustive training.
3Measurement precision
If linguistic analysis is applied to filter invalid hypotheses, then hypothesis accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent implements preliminary grammar testing as a fast filter before applying slower linguistic analysis. This preliminary action quickly identifies hypotheses that are likely to fail, allowing the system to apply resource-intensive linguistic analysis only to cases where it is most needed, thereby maintaining accuracy while improving processing speed.
Solution Approach 2:
The patent applies linguistic analysis partially, only to hypotheses that fail grammar tests and have high confidence scores, rather than applying it universally. This selective application maintains hypothesis accuracy for problematic cases while significantly improving overall processing speed by avoiding redundant analysis of already-valid hypotheses.
Data Source
AI summary
Processing data for speech recognition by generating hypotheses from input data, assigning each hypothesis, a score according to a confidence level value and hypothesis ranking, executing a pass/fail grammar test against each hypothesis, generating replacement hypotheses according to grammar test failures, assigning each replacement hypothesis a score according to a number of hypothesis changes, and providing a set of hypotheses, wherein the set comprises at least one replacement hypotheses.