Speaker Recognition Model Adaptation Without Re-Enrollment
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Solution Overview
Problem
Existing natural language processing systems require users to perform multiple enrollment processes for different words and devices, which is inconvenient and inefficient.
Innovation Solution
The system generates a first speaker recognition model for a user based on enrollment data, and then uses this model to create a second speaker recognition model specific to a new word or device, without requiring additional enrollment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple enrollment processes are required for different words and devices, then speaker recognition accuracy can be maintained, but user convenience and system efficiency deteriorate
Solution Approach 1:
The system employs a universal speaker recognition model that can recognize multiple words and adapt to different devices without requiring separate enrollment processes for each word-device combination. This single model serves multiple functions across different linguistic and device contexts, eliminating the need for repeated enrollments while maintaining recognition accuracy.
Solution Approach 2:
The system performs preliminary adaptation by pre-processing and storing speaker characteristics in a comprehensive manner during the initial enrollment. This preliminary action enables the model to handle various words and devices without requiring additional enrollment steps, as the foundational speaker model is already prepared and can be dynamically adapted.
2Measurement precision
If multiple enrollment processes are required for different words and devices, then speaker recognition precision can be maintained, but time consumption increases
Solution Approach 1:
A single universal speaker recognition model replaces multiple specialized models that would be needed for different words and devices. This universal model maintains precision by incorporating diverse training data and adaptation mechanisms, while significantly reducing time consumption by eliminating the need for multiple sequential enrollment processes.
Solution Approach 2:
The system dynamically adjusts model parameters and adaptation thresholds based on the specific word and device context without requiring re-enrollment. By changing parameters such as recognition thresholds and adaptation weights rather than performing complete re-enrollment, the system maintains precision while reducing time consumption.
3Reliability
If separate speaker recognition models are created for each word and device, then recognition accuracy for specific contexts is improved, but system complexity increases
Solution Approach 1:
The system uses a single universal speaker recognition model that handles multiple words and devices through adaptive mechanisms rather than creating separate models for each combination. This universal approach reduces system complexity by consolidating multiple models into one while maintaining context-specific accuracy through dynamic adaptation.
Solution Approach 2:
The system segments the speaker recognition task into two parts: a universal speaker model that captures individual speaker characteristics, and a separate adaptation layer that handles word and device-specific context. This segmentation allows the system to maintain context-specific accuracy without the complexity of managing multiple complete models, as only the adaptation parameters need to be adjusted.
Data Source
AI summary
Techniques for generating, from first speaker recognition data corresponding to at least a first word, second speaker recognition data corresponding to at least a second word are described. During a speaker recognition enrollment process, a device receives audio data corresponding to one or more prompted spoken inputs comprising the at least first word. Using the prompted spoken input(s), the first speaker recognition data (specific to that least first word) is generated. Sometime thereafter, a user may indicate that speaker recognition processing is to be performed using at least a second word. Rather than have the user go through the speaker recognition enrollment process a second time, the device (or a system) may apply a transformation model to the first speaker recognition data to generate second speaker recognition data specific to the at least second word.