Speech Recognition Disambiguation Feedback Loop
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing voice control technologies often misinterpret user voice commands due to variations in pronunciation, leading to incorrect understanding and execution of intended actions, particularly in tasks like online shopping.
Innovation Solution
Implementing a system that detects automatic speech recognition errors by assessing confidence thresholds and requesting user feedback through audio or visual prompts to clarify intended actions, updating user-specific and general speech recognition models to improve interpretation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems rely solely on automatic speech recognition without user feedback, then the system operates with simpler architecture and faster initial response, but speech recognition accuracy deteriorates due to pronunciation variations and misinterpretations
Solution Approach 1:
The system implements a feedback mechanism where users can provide corrections when speech recognition is inaccurate. The feedback processing module receives user feedback about misrecognized speech and uses it to update the speech recognition model, thereby improving accuracy while maintaining a manageable system architecture through modular design.
Solution Approach 2:
The system performs preliminary speech recognition before final confirmation. The automatic speech recognition module initially transcribes speech, and then the system prepares multiple possible interpretations with confidence scores before presenting options to the user, allowing for early error detection and correction.
2Measurement precision
If the system requests user feedback for every speech recognition instance, then speech recognition accuracy improves through continuous learning, but user interaction time and system response time increase
Solution Approach 1:
The system applies feedback requests selectively rather than universally. The feedback processing module determines whether to request feedback based on confidence scores from speech recognition - only low-confidence or ambiguous recognitions trigger feedback requests, while high-confidence recognitions proceed directly without user intervention.
3Measurement precision
If speech recognition models are updated frequently with user feedback, then recognition accuracy improves continuously, but system stability and model reliability may deteriorate due to overfitting or noisy data
Solution Approach 1:
Before updating speech recognition models with feedback data, the system performs preliminary validation to ensure data quality. The feedback processing module filters and validates user feedback to remove noisy or incorrect corrections, and aggregates feedback across multiple users before model updates to prevent overfitting to individual quirks.
Data Source
AI summary
A request including audio data is received from a voice-enabled device. A string of phonemes present in the utterance is determined through speech recognition. At a later time, a subsequent user input corresponding to the request may be received, in which the user input is associated with one or more text keywords. The subsequent user input may be obtained in response to an active request. Alternatively, feedback may not be actively elicited, but rather collected passively. However it is obtained, the one or more keywords associated with the subsequent user input may be associated with the string of phonemes to indicate that the user is saying or mean those words when they product that string of phonemes. A user-specific speech recognition key for the user account is then updated to associate the string of phonemes with these words. A general speech recognition model can also be trained using the association.