Domain-Specific Language Model Components for Speech Recognition
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Solution Overview
Problem
Current language models face challenges in accurately incorporating non-linguistic context for speech recognition, such as sparsity of domain-specific training data and improper influence of domain information, leading to reduced performance and increased complexity.
Innovation Solution
A language model structure comprising a domain-independent baseline model and domain-specific components, where the baseline model is trained first and kept constant, while domain-specific components are trained separately using the baseline model's output, focusing on frequently occurring n-grams and dynamically selecting components based on non-linguistic context for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific model components are trained separately using the baseline model's output, then speech recognition accuracy for specific domains is improved, but the complexity of the overall model structure increases
Solution Approach 1:
The language model is divided into a domain-independent baseline model and multiple domain-specific components. Each domain-specific component is trained separately using the baseline model's output, allowing specialized accuracy for each domain while maintaining a shared foundational structure that reduces overall complexity.
Solution Approach 2:
The baseline model and domain-specific components are merged into a unified language model structure. The baseline model serves as a common foundation that is combined with selected domain-specific components based on the input data, achieving both improved accuracy and managed complexity through integration.
2Adaptability or versatility
If domain-specific model components are trained using limited domain-specific training data, then the model adapts to specific domains, but the sparsity of training data reduces the effectiveness of domain-specific training
Solution Approach 1:
The baseline model is trained first on extensive general-domain data to establish a robust foundation of language understanding. This preliminary training ensures that when domain-specific components are trained on sparse domain-specific data, they build upon a already-optimized foundation, significantly improving the effectiveness of limited domain-specific training data.
Solution Approach 2:
The baseline model acts as an intermediary between general language knowledge and domain-specific training data. It transfers and amplifies the value from limited domain-specific examples by grounding them in the broader context provided by the pre-trained baseline model, effectively bridging the gap between data scarcity and adaptation requirements.
3Adaptability or versatility
If the baseline model parameters are updated during domain-specific training, then the model can adapt to different domains, but the domain-specific training may degrade the baseline model's performance on general language tasks
Solution Approach 1:
The model parameters are segmented into two distinct sets: baseline model parameters that remain fixed during domain-specific training, and domain-specific component parameters that are updated. This segmentation allows domain-specific adaptation without risking degradation of general language performance, as the baseline parameters are protected from updates.
Solution Approach 2:
Domain-specific components are trained as separate parameter sets that copy the structure of the baseline model but operate independently. This copying approach allows domain-specific training to occur without modifying the original baseline model, preserving general language performance while enabling domain adaptation through the copied component structures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for language models using domain-specific model components. In some implementations, context data for an utterance is obtained. A domain-specific model component is selected from among multiple domain-specific model components of a language model based on the non-linguistic context of the utterance. A score for a candidate transcription for the utterance is generated using the selected domain-specific model component and a baseline model component of the language model that is domain-independent. A transcription for the utterance is determined using the score the transcription is provided as output of an automated speech recognition system.