Rewriting ASR Output for Accurate NLU Actions
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Solution Overview
Problem
Speech processing systems often fail to accurately interpret user inputs due to errors in automatic speech recognition (ASR) and natural language understanding (NLU), leading to incorrect actions being performed by skills, which can be frustrating for users and reduce system responsiveness.
Innovation Solution
The system employs machine learning models trained with user feedback to rewrite user inputs, addressing transcription errors, entity disambiguation, intent classification, and user errors by leveraging explicit and implicit feedback to improve ASR and NLU processing, ensuring that user inputs are correctly interpreted and actions are accurately performed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If speech processing systems use automatic speech recognition and natural language understanding to interpret user inputs, then the system can perform tasks based on spoken commands, but transcription errors and misinterpretations occur leading to incorrect actions
Solution Approach 1:
The system employs feedback mechanisms where user corrections and confirmations are captured and used to retrain the ASR and NLU models. When users correct misinterpreted inputs or confirm correct interpretations, this feedback data is fed back into the system to improve future accuracy, creating a continuous learning loop that reduces transcription errors and misinterpretations.
Solution Approach 2:
The system performs self-correction by automatically learning from user interactions and feedback. The machine learning models continuously improve their own performance by processing user corrections and adapting to individual user speech patterns, making the system progressively more accurate without requiring manual reconfiguration.
2Measurement precision
If the system uses machine learning models to rewrite user inputs, then the accuracy of speech recognition and natural language understanding improves, but the complexity of the system increases
Solution Approach 1:
The system pre-trains machine learning models with extensive speech data and natural language patterns before deployment. This preliminary training allows the models to handle a wide range of speech variations and language structures, improving accuracy without requiring complex real-time processing during actual user interactions.
Solution Approach 2:
The machine learning models act as intermediaries between the raw speech input and the system's action execution. These models translate and normalize user inputs into a standardized format that the system can reliably process, effectively mediating between the variability of human speech and the structured requirements of the control system.
3Reliability
If the system processes and rewrites user inputs through multiple stages, then the likelihood of incorrect actions decreases, but the response time may increase
Solution Approach 1:
The system applies partial processing stages selectively based on input confidence levels. For high-confidence, clear inputs, the system uses minimal processing to maintain fast response times. For ambiguous or low-confidence inputs, additional processing stages are applied to ensure accuracy, thus balancing response time with correctness on a case-by-case basis.
Solution Approach 2:
The system performs preliminary filtering and confidence assessment of user inputs before full processing. By quickly evaluating input quality and applying appropriate processing levels, the system avoids unnecessary processing steps for clear inputs, maintaining responsiveness while ensuring thorough processing when needed.
Data Source
AI summary
Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed. If the trained machine learning model(s) indicates the present ASR output text data corresponds (or substantially corresponds) to such previous ASR output text data, the system may rewrite the present ASR output text data to correspond to text data representing a rephrase of the user input that will (or is more likely to) result in a correct action being performed.