Dynamic Speaker Verification Threshold Adaptation
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Solution Overview
Problem
Current speaker verification systems face challenges in accurately identifying authorized users in varying environmental contexts, leading to false rejections and acceptances, as they struggle to adapt to changes in noise levels and user voice patterns.
Innovation Solution
A method for adapting speaker-dependent hotword models by using a system that includes a computing device, a server, and modules like endpointers, neural networks, and sample clusterers to analyze audio data, compute confidence scores, and update voice templates based on user feedback, thereby improving recognition performance across different environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold is used for speaker verification, then the system is simple to operate, but it leads to false rejections and acceptances in varying environmental contexts
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously adapting the speaker verification threshold based on environmental context and historical data. Instead of using a fixed threshold, the system modifies thresholds in real-time to accommodate varying noise levels and acoustic conditions, thereby maintaining high verification accuracy across different environments without requiring complex manual reconfiguration
Solution Approach 2:
The system incorporates feedback mechanisms where verification results and environmental data are fed back into the threshold adaptation algorithm. This feedback loop allows the system to learn from past verification outcomes and environmental conditions, continuously refining the threshold to minimize false rejections and acceptances while maintaining operational simplicity
2Productivity
If the system always listens for hotword, then the user can quickly invoke voice commands, but the device consumes continuous energy for processing
Solution Approach 1:
The patent implements periodic hotword detection where the device listens for the hotword at scheduled intervals rather than continuously processing all audio. This periodic approach maintains quick invocation capability by being ready at regular intervals while significantly reducing energy consumption by allowing the processing unit to enter low-power states between detection periods
Solution Approach 2:
The system uses self-service mechanisms where the hotword detection module autonomously manages its own activation and deactivation based on detected patterns. The device can automatically adjust its listening behavior based on usage patterns and environmental cues, maintaining responsiveness while optimizing energy usage without requiring constant user intervention or high-power continuous processing
3Adaptability or versatility
If speaker verification uses fixed voice templates, then the model is easy to create, but it fails to adapt to changes in user voice patterns
Solution Approach 1:
The patent implements preliminary voice template creation during a setup phase where the user's voice characteristics are captured and stored before actual use. This preliminary action establishes a baseline template that can later be adapted, allowing the system to quickly initialize verification without complex real-time analysis while still enabling future adaptation to voice pattern changes
Solution Approach 2:
The system continuously compares actual voice samples against stored templates and uses feedback from verification outcomes to incrementally update the templates. This feedback-driven adaptation allows the voice models to evolve and accommodate changes in user voice patterns over time, such as due to aging or health conditions, while maintaining a relatively simple update mechanism based on gradual adjustments rather than complete reconfiguration
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a dynamic threshold for speaker verification are disclosed. In one aspect, a method includes the actions of receiving, for each of multiple utterances of a hotword, a data set including at least a speaker verification confidence score, and environmental context data. The actions further include selecting from among the data sets, a subset of the data sets that are associated with a particular environmental context. The actions further include selecting a particular data set from among the subset of data sets based on one or more selection criteria. The actions further include selecting, as a speaker verification threshold for the particular environmental context, the speaker verification confidence score. The actions further include providing the speaker verification threshold for use in performing speaker verification of utterances that are associated with the particular environmental context.