Speech Recognition Interface for Streaming Query Selection
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Solution Overview
Problem
Users face difficulties in accessing relevant documents during conversations due to challenges in automated speech recognition accurately determining search terms, leading to inadequate real-time information retrieval solutions.
Innovation Solution
A system that analyzes audio data to produce word hypotheses and displays them on a graphical interface at varying speeds, allowing users to select relevant terms for information retrieval, combining automated speech recognition with manual user guidance to improve accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated speech recognition is used to determine search terms from human speech, then information retrieval can be automated, but recognition errors occur and correct information may be overlooked
Solution Approach 1:
The system applies different processing qualities to different speech recognition results. High-confidence matches are processed automatically with high speed, while low-confidence matches are presented to users for manual selection. This local differentiation of processing quality resolves the contradiction by maintaining automation for reliable cases while applying human judgment only where needed.
Solution Approach 2:
The system incorporates user feedback by allowing users to select or correct speech recognition results in real-time. This feedback loop improves the overall accuracy of search term recognition while maintaining automation for the majority of cases, resolving the contradiction between automation extent and recognition precision.
2Measurement precision
If multiple word hypotheses are displayed simultaneously to allow user selection, then recognition accuracy improves, but interface clutter increases
Solution Approach 1:
The interface displays multiple word hypotheses with differentiated visual prominence based on confidence levels. High-confidence matches are displayed prominently and automatically selected, while lower-confidence alternatives are displayed with reduced prominence for user review only if needed. This local quality differentiation reduces visual clutter while maintaining accurate term selection.
Solution Approach 2:
The system extracts and displays only the most relevant word hypotheses based on confidence thresholds, rather than showing all possible interpretations. This selective extraction reduces interface clutter while preserving the accuracy benefits of multiple hypothesis evaluation by showing only the most plausible options.
3Loss of time
If speech recognition results are processed in real-time during conversation, then information retrieval keeps pace with discussion, but processing speed requirements increase
Solution Approach 1:
The system processes speech recognition results partially in real-time, immediately handling high-confidence matches with automated extraction and search initiation. Lower-confidence results are processed with less urgency, allowing users to review them after the initial information retrieval. This partial real-time processing reduces the overall speed requirement while maintaining timely information delivery for critical terms.
Solution Approach 2:
The speech recognition results are segmented into different confidence levels and processed through different pipelines. High-confidence segments are processed rapidly through automated extraction, while lower-confidence segments are handled with extended timing for user review. This segmentation allows the system to meet real-time requirements for critical information while using more relaxed timing for less certain matches.
Data Source
AI summary
Methods and systems for information retrieval include analyzing audio data to produce word hypotheses. Displaying the word hypotheses in motion at different respective speeds at once across a graphical display. Information is retrieved in accordance with one or more selected terms from the displayed word hypotheses.