Speech Recognition Re-scoring with Domain Knowledge
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Solution Overview
Problem
Current speech recognition systems often fail to accurately identify transcriptions due to lack of domain-specific information, leading to incorrect rankings of candidate transcriptions, as they cannot effectively utilize context and domain knowledge to distinguish between acoustically confusable sentences.
Innovation Solution
A speech recognition system that re-scores candidate transcriptions using domain-specific information by analyzing search results and query submission frequencies, providing feature scores to a trained classifier to improve transcription accuracy by leveraging domain-specific features and search result relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general speech recognition system is used, then the system is simple and fast, but transcription accuracy deteriorates due to inability to utilize domain-specific information
Solution Approach 1:
The patent introduces an intermediary domain knowledge module that acts as a mediator between the speech recognition system and domain-specific information sources. This module retrieves domain-specific data (such as medical terminology, legal phrases, or technical jargon) and integrates it into the speech recognition process, thereby improving transcription accuracy without requiring complete system redesign. The intermediary approach allows the system to leverage external domain knowledge while maintaining the core speech recognition architecture.
Solution Approach 2:
The speech recognition system is segmented into multiple independent components: the base speech recognition engine, the domain knowledge retrieval module, and the transcription integration layer. This segmentation allows each component to specialize in specific tasks - the base engine handles general speech-to-text conversion, while the domain knowledge module handles context-specific terminology. This modular structure improves accuracy for domain-specific content while keeping the overall system manageable and extensible.
2Measurement precision
If domain-specific information is integrated into speech recognition, then transcription accuracy for specialized content improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-loading and indexing domain-specific knowledge bases before speech recognition tasks begin. Domain terminology, context patterns, and specialized vocabularies are prepared in advance and stored in accessible formats. When speech input is received, the system quickly queries these pre-prepared domain resources rather than processing everything in real-time, thereby improving domain-specific accuracy while minimizing the computational complexity during actual speech recognition operations.
3Reliability
If multiple candidate transcriptions are evaluated using domain knowledge, then the likelihood of selecting the correct transcription improves, but processing time increases
Solution Approach 1:
The system applies partial action by selectively evaluating candidate transcriptions based on domain relevance. Rather than exhaustively analyzing all possible transcriptions equally, the domain knowledge module identifies and prioritizes candidates that align with domain-specific patterns and terminology. This selective evaluation approach maintains high reliability in selecting correct transcriptions while reducing overall processing time by focusing computational resources on the most promising candidates.
Data Source
AI summary
In some implementations, data that indicates multiple candidate transcriptions for an utterance is received. For each of the candidate transcriptions, data relating to use of the candidate transcription as a search query is received, a score that is based on the received data is provided to a trained classifier, and a classifier output for the candidate transcription is received. One or more of the candidate transcriptions may be selected based on the classifier outputs.