Headset Dialog Noise Suppression via Confidence Metrics
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Solution Overview
Problem
Generating contextual responses in noisy environments is challenging due to difficulties in determining the noise level and providing appropriate responses to users.
Innovation Solution
The solution involves using audio-environment metrics to determine noise levels and employing a model-based dialog system that trains lightweight models to optimize task success by incorporating noise, wind noise, and speech activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the assistant system operates in noisy environments without noise suppression, then the device complexity is reduced, but the speech recognition accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary noise suppression model that processes audio signals between the microphone and the speech recognition system. This model analyzes audio-environment metrics and suppresses noise components, thereby improving speech recognition accuracy without requiring fundamental changes to the core recognition system architecture.
Solution Approach 2:
The patent segments the audio processing pipeline into distinct functional components: audio signal acquisition, audio-environment metric determination, noise suppression processing, and speech recognition. This segmentation allows the noise suppression function to be added as a modular component, reducing the impact on overall device complexity while improving recognition accuracy.
2Loss of information
If the assistant system provides detailed feedback about noise levels, then the information completeness is improved, but the response time increases
Solution Approach 1:
The patent implements partial feedback by providing noise level information selectively based on the noise suppression confidence score. When the confidence score indicates high reliability, detailed noise level feedback is provided. When confidence is low, the system provides minimal or no feedback, thereby reducing response time while maintaining information completeness where it matters most.
Solution Approach 2:
The system performs preliminary noise suppression processing and confidence assessment before generating the final speech recognition result. This preliminary action allows the system to determine in advance whether detailed noise level feedback should be provided, optimizing the response time by avoiding unnecessary feedback generation steps.
3Reliability
If the assistant system uses confidence scores to filter low-confidence outputs, then the reliability is improved, but the productivity decreases
Solution Approach 1:
The patent implements dynamic filtering based on confidence scores, where the threshold for accepting speech recognition outputs adjusts according to the current noise level and suppression confidence. In high-confidence scenarios, the system accepts more outputs to maintain productivity. In low-confidence scenarios, stricter filtering is applied to maintain reliability, creating a dynamic balance between the two competing objectives.
4Adaptability or versatility
If the assistant system incorporates audio-environment metrics, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent designs the audio-environment metric determination module to serve multiple functions: noise level assessment, speech activity detection, and feedback generation. By making this component multi-functional, the system achieves high adaptability to different acoustic environments without proportionally increasing device complexity, as the same hardware and software resources are utilized for multiple purposes.
Data Source
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AI summary
A method includes accessing audio signals corresponding to a voice input from a user from a head-mounted device associated with the user, determining audio-environment metrics corresponding to the voice input based on the audio signals, calculating a confidence score indicating a likelihood that an assistant system associated with the head-mounted device understood the voice input, generating a response based on the confidence score and the audio-environment metrics, and providing instructions to the head-mounted device for presenting the response.