Word-Lattice Grammar Biasing for Context-Aware Speech Recognition
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Solution Overview
Problem
Existing speech recognition systems struggle to accurately identify user intent from ambiguous speech inputs due to the lack of effective grammar selection based on context, leading to increased latency and resource consumption when incorrect actions are performed.
Innovation Solution
A speech processing system that generates a word lattice from audio data, determines context, and adjusts grammar confidence scores to select the most likely grammar and transcription combination, using normalization techniques to prioritize accurate intent recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses multiple grammars to handle ambiguous speech inputs, then the accuracy of intent recognition is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system dynamically adjusts grammar confidence scores by applying context-dependent biasing factors. Each grammar receives a confidence score that is modified based on contextual relevance, allowing the system to prioritize more likely interpretations without exhaustively processing all possible grammars. This parameter adjustment resolves the contradiction by improving recognition accuracy through selective scoring rather than exhaustive analysis.
Solution Approach 2:
The system performs preliminary context analysis before final grammar selection. By determining contextual biasing factors in advance and applying them to grammar confidence scores, the system prepares the scoring framework proactively. This preliminary action filters out less likely grammars early in the process, reducing computational complexity while maintaining accuracy in the final selection.
2Reliability
If the system analyzes multiple candidate transcriptions with multiple grammars, then the reliability of speech recognition is improved, but the latency increases
Solution Approach 1:
The system modifies confidence scores by applying contextual biasing factors that amplify the differences between high-probability and low-probability grammars. This parameter change allows the system to reach a reliable decision more quickly by creating a clearer separation between correct and incorrect interpretations, thereby reducing the time needed to achieve reliable recognition.
Solution Approach 2:
The system applies contextual biasing to focus computational resources on the most relevant grammars rather than exhaustively analyzing all candidate transcriptions and grammars equally. By partially processing the full search space through selective biasing, the system achieves reliable recognition with reduced latency compared to complete exhaustive analysis.
3Measurement precision
If the system uses context-based grammar selection, then the accuracy of transcription selection is improved, but the processing overhead increases
Solution Approach 1:
The system applies lightweight contextual biasing factors that modify grammar confidence scores through simple multiplicative or additive adjustments rather than complex re-analysis. This parameter change approach improves transcription selection accuracy by incorporating context while maintaining low processing overhead through efficient score modification.
Solution Approach 2:
The system uses pre-computed contextual biasing factors that can be reused across multiple grammar evaluations. By copying and applying the same contextual factors to different grammars rather than re-analyzing context for each grammar, the system improves accuracy through consistent contextual application while minimizing processing overhead through efficient factor reuse.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing contextual grammar selection are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance. The actions include generating a word lattice that includes multiple candidate transcriptions of the utterance and that includes transcription confidence scores. The actions include determining a context of the computing device. The actions include based on the context of the computing device, identifying grammars that correspond to the multiple candidate transcriptions. The actions include determining, for each of the multiple candidate transcriptions, grammar confidence scores that reflect a likelihood that a respective grammar is a match for a respective candidate transcription. The actions include selecting, from among the candidate transcriptions, a candidate transcription. The actions further include providing, for output, the selected candidate transcription as a transcription of the utterance.