Dynamic Hint Word Lists for Automated Speech Recognition
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Solution Overview
Problem
Existing automated speech recognition (ASR) systems face challenges with limited accuracy due to difficulties in distinguishing words that sound similar or are mispronounced, and the current methods for updating hint words are inefficient, leading to a large and unmanageable list.
Innovation Solution
A dynamic system for generating and updating a sliding window of hint words based on qualifier rules, including syllable count, phonetic and rhyming matches, and difficulty scores, to improve ASR accuracy by incorporating terms from user interactions and overheard content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hint words are continuously added from captured terms and conversations, then ASR accuracy is improved through better context, but the hint word list grows too large and becomes unmanageable
Solution Approach 1:
The system implements a sliding window mechanism that automatically discards oldest hint words when the list reaches maximum capacity, while retaining recently captured terms. This ensures the hint word list remains manageable in size while continuously incorporating new vocabulary from conversations and content, thus maintaining ASR accuracy without unbounded growth.
Solution Approach 2:
The hint word list is transformed from a static structure to a dynamic sliding window that automatically adjusts its contents over time. The window slides forward by removing oldest entries and adding new captured terms, creating a living vocabulary that adapts to changing context while maintaining a fixed size constraint.
2Adaptability or versatility
If a large number of hint words are maintained to cover diverse vocabulary, then recognition of challenging words is improved, but processing complexity and memory requirements increase
Solution Approach 1:
By systematically discarding oldest hint words and recovering only the most recently captured terms, the system maintains a fixed-size window that provides diverse vocabulary coverage without the cumulative complexity burden of an ever-growing list. This selective retention strategy balances adaptability with processing efficiency.
3Reliability
If the hint word list is updated frequently with new terms, then context relevance is improved, but the time and computational resources required for updates increase
Solution Approach 1:
The sliding window creates a dynamic, automatically updating hint word list that maintains context relevance through continuous sliding rather than frequent manual updates. The system naturally evolves by removing oldest entries and adding new terms, reducing the need for intensive periodic update operations while maintaining high context relevance.
Data Source
AI summary
Systems and methods are provided for determining hint words that improve the accuracy of automated speech recognition (ASR) systems. Hint words are typically determined in the context of a user issuing voice commands in connection with a voice interface system, however, a voice interface system may capture terms from overheard content and/or conversations. A system may determine a sliding window of hint words using set of qualifier rules. The system may capture audio, e.g., from a conversation or played back content, as a first input and decipher a plurality of words including a qualifying first term added to the hint words. The voice interface system may capture more audio as a second input and decipher a second plurality of words including a qualifying second term. The first term may be removed from the set of hint words, e.g., when the second term is added or after an expiration time.