Speaker Verification Module Parameter Adaptation
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Solution Overview
Problem
Speaker identification systems face reliability issues due to insufficient training data that fails to encompass intra-speaker variability and highlight inter-speaker variability, leading to high false alarm and missed target rates in deployed voice-based interactive systems.
Innovation Solution
The system updates its parameters using actual field audio data, specifically employing i-vectors generated from field data to adapt the i-vector based speaker verification module, allowing for improved recognition accuracy over time with minimal overhead in disk occupancy and computational complexity, while maintaining system security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speaker identification systems use parameters generated during pre-deployment enrollment phase, then the system can be initially configured, but the reliability of speaker identification deteriorates due to insufficient training data that fails to encompass intra-speaker variability and highlight inter-speaker variability
Solution Approach 1:
The system performs preliminary classifier adaptation during the enrollment phase using available training data to establish initial speaker parameters. This preliminary configuration enables the system to function immediately while continuing to accumulate field data for future refinement, thus resolving the contradiction between having sufficient training data and maintaining initial system configuration.
Solution Approach 2:
The system transitions from static parameters established during enrollment to dynamic parameter adaptation using ongoing field data. The classifier is continuously refined using actual audio data collected during deployment, allowing the system to adapt to real-world intra-speaker variability and improve reliability over time without requiring complete retraining.
2Measurement precision
If actual field audio data is used to update classifier parameters, then speaker recognition accuracy improves, but system complexity and computational overhead increase
Solution Approach 1:
The system implements a feedback mechanism where classification results from field audio data are fed back to update classifier parameters. The adaptation module uses verification outcomes and accumulated field data to iteratively refine speaker parameters, improving accuracy while maintaining a manageable complexity through automated feedback loops rather than manual system redesign.
Solution Approach 2:
The classifier performs self-adaptation using its own accumulated field data and verification results. The system automatically updates its parameters without requiring external intervention or complex external training infrastructure, reducing overall system complexity while continuously improving speaker recognition accuracy through self-learning.
3Reliability
If classifier parameters are adapted using field data, then false alarm and missed target rates reduce, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential adaptation information from field audio data - specifically, updated speaker parameters and classification statistics - rather than storing all raw field audio. This extraction approach reduces storage requirements while maintaining the ability to improve false alarm and missed target rates through parameter adaptation.
Solution Approach 2:
The system transforms raw field audio data into condensed parameter representations that capture the essential variability information needed for adaptation. By changing from storing extensive audio data to storing refined parameter sets, the system reduces storage requirements while maintaining improved reliability through accurate speaker parameter updates.
Data Source
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AI summary
Typical speaker verification systems usually employ speakers' audio data collected during an enrollment phase when users enroll with the system and provide respective voice samples. Due to technical, business, or other constraints, the enrollment data may not be large enough or rich enough to encompass different inter-speaker and intra-speaker variations. According to at least one embodiment, a method and apparatus employing classifier adaptation based on field data in a deployed voice-based interactive system comprise: collecting representations of voice characteristics, in association with corresponding speakers, the representations being generated by the deployed voice-based interactive system; updating parameters of the classifier, used in speaker recognition, based on the representations collected; and employing the classifier, with the corresponding parameters updated, in performing speaker recognition.