Back-Off Grammar for Out-of-Grammar Speech Recognition
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Solution Overview
Problem
Current speech recognition applications using context-free grammars often fail to recognize out-of-grammar utterances, leading to low confidence or no recognition results, which can result in incomplete tasks and user dissatisfaction.
Innovation Solution
A system that generates back-off grammar rules to process out-of-grammar utterances by identifying keywords and slots, adding filler tags, and updating the context-free grammar with newly recognized utterances to improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If context-free grammar rules are used for speech recognition, then recognition speed and performance are improved, but out-of-grammar utterances cannot be recognized leading to low accuracy
Solution Approach 1:
The grammar recognition system is segmented into two distinct components: a context-free grammar (CFG) module for recognizing standard pre-defined phrases, and a back-off grammar (BOG) module for handling out-of-grammar utterances. This segmentation allows each module to specialize - the CFG module maintains high-speed recognition for common phrases while the BOG module provides fallback recognition for novel expressions, thereby resolving the contradiction between speed and accuracy.
Solution Approach 2:
The back-off grammar module acts as an intermediary between the CFG module and the final recognition result. When the CFG module fails to recognize an utterance or produces low-confidence results, the BOG module intervenes to process the out-of-grammar utterance through keyword identification and filler tag addition, ensuring that no valid utterance is rejected due to grammatical constraints.
2Reliability
If grammar coverage is expanded to recognize more utterances, then recognition accuracy is improved, but grammar size increases reducing performance
Solution Approach 1:
The grammar system transitions from a static, fixed grammar structure to a dynamic, adaptive structure. The personalization component dynamically updates the CFG by incorporating frequently used OOG utterances from individual users, while simultaneously pruning rarely used phrases. This dynamic adjustment allows the grammar to grow in coverage for each user without uniformly increasing system-wide complexity, maintaining performance while improving accuracy for the specific user base.
3Productivity
If a fixed grammar is used, then system performance is maintained, but user-specific language preferences cannot be accommodated
Solution Approach 1:
The personalization component implements a feedback mechanism that continuously monitors user speech patterns and grammar usage. When OOG utterances are successfully recognized by the BOG module, the system feeds this information back to update the CFG, incorporating user-specific phrases and expressions. This feedback loop enables the system to adapt to individual user preferences over time while maintaining overall performance through selective grammar updates rather than comprehensive reconfiguration.
Data Source
AI summary
Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.