Dynamic Voice Profile Updating via Audio Clustering
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Solution Overview
Problem
Speech recognition systems face challenges in accurately identifying users over time due to changes in voice characteristics and variations in device and environmental conditions, leading to misrecognition of user inputs.
Innovation Solution
The system updates user voice profiles using clustering techniques and context signals, including user verification information and usage history, to adapt to changes in speech characteristics and device conditions, ensuring accurate user identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice profiles are kept static over time, then system complexity is reduced, but user recognition accuracy deteriorates due to voice changes from aging and sickness
Solution Approach 1:
The voice profile is transformed from a static representation to a dynamic one that automatically adapts over time. The system periodically receives additional audio data and updates the voice profile accordingly, allowing it to evolve with the user's voice characteristics while maintaining recognition accuracy without requiring manual intervention
Solution Approach 2:
The voice profile updating process is automated and self-service oriented. The system autonomously collects audio data, performs clustering analysis, and updates the voice profile without requiring user action. The user simply continues to interact with the device normally, and the system handles the adaptation process in the background
2Measurement precision
If voice profiles are updated frequently, then user recognition accuracy is maintained despite voice changes, but system resource consumption increases
Solution Approach 1:
The voice profile updates occur periodically rather than continuously. The system receives audio data at specific intervals and performs updates at these periodic points, balancing the need for accuracy with resource conservation. This periodic approach ensures the profile stays current while avoiding excessive processing
Solution Approach 2:
The system changes the parameters of the voice profile based on incoming audio data. By analyzing variations in voice characteristics and selectively updating profile parameters when significant changes are detected, the system maintains accuracy while minimizing unnecessary processing and resource consumption
3Adaptability or versatility
If clustering techniques are used to update voice profiles, then adaptability to voice changes is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential features from audio data that are relevant for voice profile updating. By focusing on key acoustic characteristics rather than processing the entire audio signal, the clustering operation becomes more computationally efficient while still capturing the necessary information for adaptation
Solution Approach 2:
The system performs clustering on a subset of audio data rather than all available data. By selecting representative samples or using incremental clustering approaches, the system achieves sufficient adaptability with reduced computational burden, applying just enough processing to maintain accuracy without excessive complexity
Data Source
AI summary
Techniques for updating voice profiles used to perform user recognition are described. A system may use clustering techniques to update voice profiles. When the system receives audio data representing a spoken user input, the system may store the audio data. Periodically, the system may recall, from storage, audio data (representing previous user inputs). The system may identify clusters of the audio data, with each cluster including similar or identical speech characteristics. The system may determine a cluster is substantially similar to an existing voice profile. If this occurs, the system may create an updated voice profile using the original voice profile and the cluster of audio data.