Selective Discriminative Adaptation for Speech Recognition
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Solution Overview
Problem
Current automatic speech recognition systems face inefficiencies in speaker adaptation, as discriminative methods provide limited overall gains but benefit specific speakers with high error rates, making it costly to apply adaptation to all users.
Innovation Solution
Implementing a system that evaluates speech recognition performance data to identify candidates for discriminative adaptation, using methods like constrained discriminative linear transform (CDLT), model-space discriminative linear transform (DLT), and discriminative maximum a-posteriori (DMAP) to apply adaptation only to speakers who will benefit significantly, thereby optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If discriminative adaptation is applied to all speakers, then recognition accuracy for high error rate speakers is improved, but computational cost increases significantly
Solution Approach 1:
The patent applies discriminative adaptation selectively to specific speakers who exhibit high error rates rather than uniformly to all speakers. The system identifies individual speakers needing adaptation and applies the computationally intensive discriminative methods only to them, while using simpler adaptation for others, thus optimizing the balance between accuracy improvement and computational cost.
Solution Approach 2:
The patent implements partial adaptation by applying discriminative adaptation only to the extent necessary for speakers with high error rates. Rather than applying full adaptation to all users, the system performs adaptation selectively and partially based on individual speaker needs, reducing overall computational expenditure while maintaining effectiveness for those who benefit most.
2Reliability
If discriminative adaptation is applied universally, then overall system performance improves, but computational resources are wasted on speakers who don't need it
Solution Approach 1:
The system implements local quality by tailoring the adaptation approach to individual speaker characteristics. Speakers with high error rates receive discriminative adaptation while others receive standard adaptation, ensuring that computational resources are allocated based on actual need rather than applied uniformly, thus improving both performance and efficiency.
Solution Approach 2:
The system enables self-service adaptation by automatically identifying which speakers need discriminative adaptation based on their error rates, and applying adaptation only to them without requiring manual intervention. This automated selective adaptation improves computational efficiency by avoiding unnecessary processing for speakers who don't need additional adaptation.
3Reliability
If adaptation is applied to all users, then recognition accuracy for difficult speakers improves, but processing time increases
Solution Approach 1:
The patent applies adaptation processing locally to only those speakers who demonstrate high error rates, rather than processing all speakers uniformly. This selective approach reduces overall processing time while maintaining accuracy improvements for the specific speakers who need them, as the computationally intensive discriminative adaptation is skipped for speakers who don't require it.
Solution Approach 2:
The system performs partial adaptation by applying discriminative methods only to the extent necessary for high error rate speakers. For speakers with already acceptable accuracy, the system uses reduced or no adaptation, thereby reducing total processing time while still achieving accuracy improvements where needed.
Data Source
AI summary
A method is described for use with automatic speech recognition using discriminative criteria for speaker adaptation. An adaptation evaluation is performed of speech recognition performance data for speech recognition system users. Adaptation candidate users are identified based on the adaptation evaluation for whom an adaptation process is likely to improve system performance.