Incremental ASR Word Correction With Domain-Specific Confidence Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automatic speech recognition (ASR) systems often struggle to accurately interpret user speech in specific domains due to deviations from intended meanings, particularly in niche applications, leading to inaccuracies in output results.
Innovation Solution
A speech recognition system that utilizes both general and domain-specific ASRs, along with sequence-aware and sequence-unaware ASRs, processes incremental speech inputs in parallel, weighting outputs based on confidence values and domain relevance to generate a composite speech output that accurately captures both domain-specific and general conversational words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single ASR system is used for speech recognition, then the system complexity is low, but the accuracy of speech recognition in specific domains deteriorates
Solution Approach 1:
The speech recognition system is segmented into multiple specialized ASR models, each trained for specific domains (e.g., medical, legal, technical). This segmentation allows each model to specialize in particular vocabulary and speech patterns, improving accuracy for domain-specific tasks while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system employs a universal framework that can dynamically select and switch between multiple domain-specific ASR models based on the detected domain of the input speech. This multi-functionality allows a single system to handle diverse speech recognition tasks across different domains without requiring separate dedicated systems for each domain.
2Measurement precision
If multiple ASRs are used to process speech inputs in parallel, then the speech recognition accuracy is improved, but the device complexity increases
Solution Approach 1:
The system dynamically adjusts the selection and weighting of multiple ASR models based on real-time domain detection and confidence scoring. Rather than always processing through all models equally, the system adapts its processing path by selecting the most appropriate model for the current domain, reducing unnecessary computational complexity while maintaining high accuracy.
Solution Approach 2:
An intermediary domain detection module and result integration component are introduced to manage the outputs from multiple parallel ASR models. This intermediary layer coordinates the parallel processing by selecting appropriate models based on domain classification and integrating their results through confidence-based weighting, thereby managing complexity while preserving the accuracy benefits of parallel processing.
3Measurement precision
If domain-specific ASR is used, then the accuracy for domain-specific words is improved, but the ability to handle general conversational speech deteriorates
Solution Approach 1:
The system creates a universal speech recognition framework that incorporates multiple domain-specific ASR models, each specialized for particular domains. This universal system can adapt to handle both domain-specific technical vocabulary and general conversational speech by selecting the appropriate model based on domain detection, thereby maintaining both specialization and versatility.
Solution Approach 2:
The system changes the operational parameters of the ASR models by dynamically adjusting which model is active based on domain detection. When domain-specific speech is detected, the corresponding specialized model is activated with appropriate parameter settings; when general conversation is detected, a general-purpose model or ensemble approach is used, allowing optimal performance across different speech types.
Data Source
AI summary
An exemplary automatic speech recognition (ASR) system may receive an audio input including a segment of speech. The segment of speech may be independently processed by general ASR and domain-specific ASR to generate multiple ASR results. A selection between the multiple ASR results may be performed based on respective confidence levels for the general ASR and domain-specific ASR. As incremental ASR is performed, a composite result may be generated based on general ASR and domain-specific ASR.