Customizable Speech Recognition Neural Network Adaptation
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Solution Overview
Problem
Conventional speech recognition systems face challenges in adapting to specific domains, as traditional back-to-back solutions require extensive manual updates and end-to-end solutions need large datasets, leading to accuracy issues when customized for particular applications.
Innovation Solution
A customizable speech recognition system using a neural network that is initially trained on a generic domain and then adapted using a small target dataset, employing techniques like freezing layers, word loss, and aligning attention weights to maintain accuracy while understanding domain-specific terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional back-to-back automatic speech recognition solutions are used, then the system can process speech through trained acoustic models and language models, but the system requires extensive manual updates and individual adaptation of multiple components to work well in new domains
Solution Approach 1:
The patent merges the acoustic model and language model into a single unified neural network architecture. This integration eliminates the need for separate adaptation of multiple components, reducing system complexity while maintaining recognition accuracy through joint training and domain adaptation.
Solution Approach 2:
The unified neural network is designed to perform multiple functions: automatic speech recognition, language modeling, and domain adaptation. This multi-functional design allows the system to handle different domains without requiring separate specialized components, reducing the overall complexity of domain adaptation.
2Ease of manufacture
If end-to-end automatic speech recognition systems are used, then the system can be trained directly on speech-to-text mapping, but the system requires extensive datasets for training and cannot be satisfactorily trained for customized systems with limited data
Solution Approach 1:
The system performs preliminary training on a large generic dataset to establish foundational speech recognition capabilities. This pre-training phase allows the model to learn general speech patterns before being adapted to specific domains with limited data, reducing the amount of domain-specific training data needed.
Solution Approach 2:
The system changes training parameters and architecture configuration during domain adaptation. By adjusting model parameters, learning rates, and training strategies based on the target domain characteristics, the system can achieve satisfactory performance with limited domain-specific data while maintaining the benefits of end-to-end training.
Data Source
AI summary
Methods and systems are provided for generating a customized speech recognition neural network system comprised of an adapted automatic speech recognition neural network and an adapted language model neural network. The automatic speech recognition neural network is first trained in a generic domain and then adapted to a target domain. The language model neural network is first trained in a generic domain and then adapted to a target domain. Such a customized speech recognition neural network system can be used to understand input vocal commands.