Automated Assistant Speech Correction via Hypothesis Confidence Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated assistants often misrecognize spoken utterances, leading to unintended actions, prolonged dialog sessions, and unnecessary resource consumption due to repeated utterances and corrections.
Innovation Solution
Implement a system that processes audio data using speech recognition models to generate multiple hypotheses, assess confidence levels, and allows for user corrections through additional spoken utterances to identify and correct misrecognitions, adapting processing parameters based on confidence and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If speech recognition is performed to convert spoken utterances to text, then automated assistant functionality is enabled, but misrecognitions occur leading to unintended actions
Solution Approach 1:
The system performs preliminary actions by generating multiple transcription hypotheses and evaluating their confidence levels before finalizing the speech recognition result. This allows the system to prepare correction mechanisms in advance, reducing the impact of misrecognitions before they lead to unintended actions.
Solution Approach 2:
The system implements feedback by monitoring confidence levels of transcription hypotheses and using user corrections to improve future speech recognition. When a misrecognition is detected through low confidence scores or user feedback, the system adjusts its processing parameters and re-evaluates hypotheses, creating a closed-loop improvement mechanism.
2Measurement precision
If multiple transcription hypotheses are generated and evaluated, then speech recognition accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system dynamically changes processing parameters such as the number of hypotheses generated, confidence thresholds, and evaluation depth based on contextual factors like noise levels, user history, and task complexity. This allows the system to optimize the balance between accuracy and computational resource consumption adaptively.
Solution Approach 2:
The system performs partial evaluation of transcription hypotheses by focusing computational resources on the most promising candidates rather than exhaustively evaluating all possibilities. This selective approach achieves sufficient accuracy while significantly reducing computational overhead.
3Reliability
If user corrections are processed through additional spoken utterances, then misrecognitions are corrected, but dialog session length increases
Solution Approach 1:
The system prepares correction mechanisms in advance by maintaining multiple transcription hypotheses and confidence level evaluations ready for rapid comparison. When a correction is needed, the system can quickly juxtapose the original and corrected transcriptions without requiring extensive additional processing, thus minimizing dialog session extension.
Solution Approach 2:
The system efficiently discards incorrect transcription hypotheses and recovers computational resources for processing corrections. By systematically eliminating low-confidence hypotheses and focusing on plausible alternatives, the system minimizes the processing overhead associated with handling user corrections.
4Reliability
If confidence levels are used to evaluate transcription hypotheses, then speech recognition reliability improves, but system complexity increases
Solution Approach 1:
The system dynamically adjusts confidence thresholds and evaluation criteria based on contextual factors such as environmental noise, user speech patterns, and task requirements. This adaptive approach allows the system to maintain high reliability while managing complexity through context-aware parameter optimization rather than rigid complex algorithms.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Implementations can receive audio data corresponding to a spoken utterance of a user, process the audio data to generate a plurality of speech hypotheses, determine an action to be performed by an automated assistant based on the speech hypotheses, and cause the computing device to render an indication of the action. In response to the computing device rendering the indication, implementations can receive additional audio data corresponding to an additional spoken utterance of the user, process the additional audio data to determine that a portion of the spoken utterance is similar to an additional portion of the additional spoken utterance, supplant the action with an alternate action, and cause the automated assistant to initiate performance of the alternate action. Some implementations can determine whether to render the indication of the action based on a confidence level associated with the action.