Dual-Loss Speech Recognition Training for Semantic Understanding
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Solution Overview
Problem
Existing speech recognition models struggle to effectively capture semantic information due to broad semantic expressions in speech samples, leading to poor recognition performance.
Innovation Solution
A method involving feature extraction, semantic extraction, and dual loss value determination is employed to train a speech recognition model, utilizing an initial model to obtain speech sample features, semantic features, and training based on first and second loss values to enhance recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an initial speech recognition model is trained using only speech recognition loss on speech samples with broad semantic expressions, then the training process is simple and fast, but the model cannot capture semantic information effectively and has poor recognition performance
Solution Approach 1:
The training process is segmented into two distinct stages: a pre-training stage using speech recognition loss for basic acoustic feature learning, and a fine-tuning stage using semantic loss for semantic information capture. This segmentation allows each stage to focus on specific objectives, improving overall recognition performance while maintaining manageable training complexity through modularization.
Solution Approach 2:
The model undergoes pre-training with speech recognition loss before the actual semantic recognition task. This preliminary action establishes a solid foundation for acoustic feature extraction and phonetic recognition, enabling the model to handle semantic expressions more effectively during subsequent fine-tuning without requiring complete retraining.
2Reliability
If semantic extraction is performed on speech sample features to capture semantic information, then the model can understand semantic expressions better, but the training process becomes more complex and computational cost increases
Solution Approach 1:
Feature extraction is performed as a preliminary action to convert speech samples into speech sample features before semantic extraction. This preliminary feature representation reduces the computational complexity of subsequent semantic processing by working with condensed feature vectors rather than raw audio signals, enabling effective semantic understanding with manageable computational resources.
Solution Approach 2:
The training process extracts and focuses only on the semantic features that are most relevant for recognition, separating them from the full speech signal. By extracting and processing only the essential semantic information from the feature space, the model achieves effective semantic understanding while minimizing unnecessary computational overhead.
3Reliability
If dual loss values (first loss based on semantic feature, second loss based on speech recognition result) are used to train the model, then both semantic understanding and recognition accuracy are improved, but the training complexity and convergence difficulty increase
Solution Approach 1:
The dual loss training is segmented into two functional components: the first loss value for semantic feature optimization and the second loss value for recognition accuracy optimization. Each loss component targets specific aspects of model performance, allowing independent optimization and simplifying the overall training process by breaking down the complex multi-objective optimization into manageable sub-problems.
Solution Approach 2:
The first loss value based on semantic features is calculated and optimized before the second loss value based on recognition results. This preliminary optimization of semantic representation establishes a foundation that facilitates subsequent recognition optimization, reducing the overall complexity of handling multiple optimization objectives simultaneously.
Data Source
AI summary
A method for training a speech recognition model includes: performing, by the speech recognition model, feature extraction on a speech sample to obtain a speech sample feature; performing semantic extraction on the speech sample feature to obtain a semantic feature of the speech sample; determining a first loss value based on the semantic feature; performing, by the speech recognition model, speech recognition on the speech sample based on the speech sample feature to obtain a speech sample recognition result; determining a second loss value based on the speech sample recognition result and a speech sample label corresponding to the speech sample; and training the speech recognition model based on the first loss value and the second loss value to obtain a trained speech recognition model.