Dialect Speech Recognition Model Adaptation
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately recognizing dialects, leading to reduced recognition accuracy for users speaking with complex dialect features, as they often rely on pre-trained models that may not account for individual dialect variations.
Innovation Solution
A processor-implemented method that generates dialect parameters using a parameter generation model, which are then applied to a trained speech recognition model to create a dialect speech recognition model, allowing for dynamic modification of the model based on the user's dialect, thereby improving recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a pre-trained speech recognition model is used, then the system is simple to implement, but recognition accuracy deteriorates for users with complex dialect features
Solution Approach 1:
The system dynamically adjusts model parameters based on detected dialect characteristics. Instead of using a static pre-trained model, the system modifies connection weights and batch normalization parameters in real-time according to the user's dialect, enabling the model to adapt to different dialectal variations while maintaining implementation simplicity.
Solution Approach 2:
The invention changes physical or chemical state, concentration, density, flexibility, etc. of object
2Measurement precision
If multiple dialect-specific models are trained separately, then recognition accuracy for each dialect is improved, but device complexity increases
Solution Approach 1:
A single speech recognition model is designed to handle multiple dialects through parameter adaptation rather than requiring separate models for each dialect. The model maintains universal applicability by adjusting its parameters based on the detected dialect characteristics, eliminating the need to manage multiple dialect-specific models.
Solution Approach 2:
A dialect detection module serves as an intermediary between the speech input and the recognition model. This mediator identifies dialect characteristics and facilitates parameter adjustment, enabling accurate dialect recognition without requiring the main recognition model to be complex or dialect-specific.
3Measurement precision
If dialect parameters are applied to all layers of the speech recognition model, then recognition accuracy is maximized, but computational overhead increases
Solution Approach 1:
Dialect parameters are applied selectively to specific layers or components of the speech recognition model rather than uniformly to all layers. This local application optimizes recognition accuracy for dialectal variations while reducing unnecessary computational overhead in layers where dialectal differences are less pronounced.
Data Source
AI summary
A speech recognition method and apparatus, including implementation and/or training, are disclosed. The speech recognition method includes obtaining a speech signal, and performing a recognition of the speech signal, including generating a dialect parameter, for the speech signal, from input dialect data using a parameter generation model, applying the dialect parameter to a trained speech recognition model to generate a dialect speech recognition model, and generating a speech recognition result from the speech signal by implementing, with respect to the speech signal, the dialect speech recognition model. The speech recognition method and apparatus may perform speech recognition and/or training of the speech recognition model and the parameter generation model.