Speech Recognition Distillation Across Domain-Mismatched Training Data
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Solution Overview
Problem
Existing speech recognition systems face challenges in training student models when the domain of available training data does not match the domain of the teacher model, particularly for long-tail languages, due to data availability and privacy concerns.
Innovation Solution
A method using knowledge distillation with domain mismatch, where a teacher ASR model generates pseudo-labels for augmented out-of-domain utterances, allowing a student ASR model to learn and adapt to the target domain without direct access to target domain training data, using techniques like Gaussian noise injection and reverberation to create additional training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge distillation is used to train student ASR models when target domain training data is unavailable, then the student model can be trained without direct access to target domain data, but the domain mismatch between available training data and teacher model reduces training effectiveness
Solution Approach 1:
The patent introduces an intermediary data augmentation process that transforms available out-of-domain training data into augmented training data that better matches the target domain characteristics. This intermediary transformation bridges the domain gap between available data and the teacher model's target domain, allowing effective knowledge distillation without direct access to target domain data.
Solution Approach 2:
The patent applies data augmentation techniques that modify training data parameters (such as adding noise, changing acoustic conditions, transforming speech characteristics) to make out-of-domain data more similar to target domain data. These parameter changes enable the student model to learn from available data while adapting to the teacher model's domain characteristics.
2Quantity of substance
If data augmentation techniques are applied to create additional training data, then the quantity of training data increases, but the complexity of the training process increases
Solution Approach 1:
The patent performs data augmentation as a preliminary step before knowledge distillation training. By pre-processing and augmenting the available training data beforehand, the system creates an enhanced training dataset that can be directly used for distillation, avoiding the need for complex real-time augmentation during training and simplifying the overall training pipeline.
Data Source
AI summary
A method includes receiving distillation data including a plurality of out-of-domain training utterances. For each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher ASR model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. The method also includes distilling a student ASR model from the teacher ASR model by training the student ASR model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher ASR model.