Adaptive Grammar Weight Adjustment for ASR-NLU Transcription Accuracy
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Solution Overview
Problem
Speech recognition and natural language understanding systems often select incorrect transcriptions due to poor human-chosen grammar and domain phrasing weights, leading to higher combined scores for incorrect interpretations over correct ones, especially when NLU scores dominate over ASR scores.
Innovation Solution
An evaluator adjusts and automatically trains the weights of weighted grammars and replaces NLU-chosen phrasings with new ones to improve transcription accuracy by determining when grammar correction is needed and implementing changes to ensure correct interpretations are favored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NLU scores are weighted heavily in the combined score calculation, then the system can improve interpretation quality, but it causes incorrect transcriptions to be selected when NLU scores for incorrect hypotheses are artificially high
Solution Approach 1:
The system dynamically adjusts the weight parameters of NLU scores based on grammar match quality. When grammar matches are poor, the NLU score weight is reduced to prevent artificial inflation of incorrect hypotheses. This parameter adjustment resolves the contradiction by making the weighting adaptive rather than fixed, allowing high NLU weighting only when grammar support is strong.
Solution Approach 2:
The system implements feedback loops where transcription selections and their associated grammar match qualities are continuously evaluated. This feedback mechanism allows the system to learn from past errors and adjust weighting parameters accordingly, preventing the same contradiction from recurring and improving both interpretation quality and selection accuracy over time.
2Device complexity
If fixed human-chosen grammar weights are used, then the system structure remains simple, but it leads to systematic errors where poor grammar phrasing causes incorrect interpretations to be favored
Solution Approach 1:
The system transitions from static, fixed grammar weights to dynamic, adaptive weights that adjust based on performance feedback. This allows the system to maintain simplicity in structure while achieving high reliability through automatic weight optimization that responds to actual transcription accuracy needs.
Solution Approach 2:
The system performs self-optimization by automatically adjusting its own grammar weights based on performance feedback without requiring manual reconfiguration. This self-service capability maintains structural simplicity while continuously improving transcription accuracy, resolving the contradiction between simplicity and reliability.
3Manufacturing precision
If manual grammar weight adjustment is performed, then changes can be made precisely, but it requires significant time and resources for evaluation and tuning
Solution Approach 1:
The system automatically performs weight adjustment based on performance feedback, eliminating the need for manual evaluation and tuning. This self-service approach achieves precise weight optimization while eliminating the time and resource costs associated with manual adjustment processes.
Solution Approach 2:
The system uses continuous performance feedback to automatically guide weight adjustments, replacing time-consuming manual evaluation with automated feedback-driven optimization. This maintains precision in weight adjustment while dramatically reducing the time and resources required.
4Measurement precision
If NLU scores for incorrect transcriptions are high due to poor grammar phrasing, then the combined score may favor incorrect hypotheses, but increasing ASR score weight would reduce reliance on problematic NLU scoring
Solution Approach 1:
The system dynamically adjusts the balance between ASR and NLU score weights based on grammar match quality. When grammar phrasing is poor, ASR weight increases and NLU weight decreases, preventing incorrect hypotheses from being favored. When grammar is strong, NLU weight increases to maintain high interpretation quality. This resolves the contradiction by making weights adaptive rather than fixed.
Data Source
AI summary
A method for configuring natural language grammars is provided to include identifying a first transcription having a first automatic speech recognition (ASR) score and a first natural language understanding (NLU) score and identifying a second transcription having a second ASR score and a second NLU score. The method includes detecting that a difference between the first and second ASR scores has a signed value with an opposite sign than a sign of a signed value of a difference between the first and second NLU scores, and responsive to detecting the opposite sign providing, to an evaluator, the audio query and the first and second transcriptions, receiving, from the evaluator, an indication of which of the first and second transcriptions is a correct transcription, and adjusting a value implemented to calculate the first NLU score or a value implemented to calculate the second NLU score.