Grammar Model Adaptation via Rescoring for Speech Recognition
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Solution Overview
Problem
Current speech recognition systems face inefficiencies in recognizing out-of-vocabulary words, requiring extensive retraining of models which is time-consuming and resource-intensive, leading to delays in adapting to new words.
Innovation Solution
A system that modifies existing grammar models to include out-of-vocabulary words by generating a rescoring model and finite state transducer, allowing the speech recognition system to recognize new words without the need for extensive retraining, by determining the difference in model weight data and generating a new grammar model compatible with existing components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive retraining of models is performed to recognize out-of-vocabulary words, then recognition accuracy is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent segments the model adaptation process into two distinct components: (1) generating a rescoring model that contains only the out-of-vocabulary words and their weight data, and (2) using a finite state transducer to efficiently integrate this rescoring model with the existing grammar model. This segmentation avoids the need to retrain the entire speech recognition model, thereby maintaining recognition accuracy while dramatically reducing adaptation time and computational resources.
Solution Approach 2:
The patent changes the approach from modifying model parameters through extensive retraining to directly generating weight data for out-of-vocabulary words. By computing weight data based on the difference between the original grammar model and the rescoring model, and then using a finite state transducer to apply these weight changes, the system achieves accurate recognition of new words without the time-consuming process of full model retraining.
2Reliability
If extensive retraining of models is performed to recognize out-of-vocabulary words, then recognition accuracy is improved, but computational resources and processing power increase significantly
Solution Approach 1:
The patent segments the computational workload by creating a separate rescoring model that handles only out-of-vocabulary words. This rescoring model is then integrated with the existing grammar model through a finite state transducer, which efficiently manages the combination without requiring full model retraining. This segmentation significantly reduces the computational resources needed while maintaining recognition accuracy.
Solution Approach 2:
The patent creates a rescoring model that is essentially a targeted copy or subset of the full model, containing only the necessary out-of-vocabulary word information and their weight data. This rescoring model is then used in conjunction with the existing grammar model, avoiding the need to copy or retrain the entire model, thereby reducing computational resource requirements while maintaining accuracy.
3Adaptability or versatility
If existing grammar models are modified to include new words, then system adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the grammar model into the original grammar model and a separate rescoring model that handles out-of-vocabulary words. The finite state transducer acts as a bridge that efficiently integrates these two segments. This segmentation approach improves system adaptability to new words while avoiding the complexity of modifying the entire grammar model structure.
Solution Approach 2:
The patent introduces a finite state transducer as an intermediary component that facilitates the integration between the original grammar model and the rescoring model. This intermediary manages the combination of the two models efficiently, allowing the system to adapt to new words without directly complicating the core grammar model structure. The finite state transducer handles the complexity of model integration while preserving the simplicity of the individual model components.
Data Source
AI summary
Systems and methods described herein relate to adapting a language model for automatic speech recognition (ASR) for a new set of words. Instead of retraining the ASR models, language models and grammar models, the system only modifies one grammar model and ensures its compatibility with the existing models in the ASR system.