Parameter-Efficient Reprogramming for Cross-Lingual Speech Recognition
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Solution Overview
Problem
Large-scale automatic speech recognition (ASR) models perform poorly for low-resource languages due to the complexity and resource-intensive nature of fine-tuning them for new languages, making them unsuitable for deployment on mobile devices.
Innovation Solution
A parameter-efficient model reprogramming method that integrates an ASR model with an input reprogramming module and a latent reprogramming module, training these modules on an adaptation dataset while keeping the ASR model's parameters frozen, allowing the model to adapt to recognizing speech in a new language without increasing its parameter size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale ASR models are tuned for low-resource languages, then speech recognition accuracy for low-resource languages is improved, but model complexity and training requirements increase significantly
Solution Approach 1:
The model is segmented into two distinct parts: a frozen pre-trained ASR model component and a trainable adaptation component. This segmentation allows the complex pre-trained model to maintain its high performance while only a small, manageable subset of parameters (in the adaptation layer) requires training for new languages, thus reducing overall training complexity while preserving accuracy.
Solution Approach 2:
Instead of training the entire model uniformly, the patent applies different treatment to different parts: the majority of the model parameters are frozen (no training), while only a specific local region (the adaptation layer with language-specific parameters) is trained. This local quality approach enables efficient adaptation to low-resource languages without the computational burden of full-model training.
2Measurement precision
If large-scale ASR models are tuned for low-resource languages, then speech recognition accuracy for low-resource languages is improved, but additional training requirements increase
Solution Approach 1:
The ASR model is pre-trained on high-resource languages beforehand to learn universal speech patterns and features. This preliminary action allows the model to have strong baseline performance across multiple languages, so that when adapting to low-resource languages, only minimal additional training is needed in the adaptation layer, significantly reducing the time required for language-specific tuning.
Solution Approach 2:
Instead of applying full training action to the entire model for each new language, the patent uses partial action by training only a small subset of parameters in the adaptation layer. This partial training approach is sufficient to achieve good performance on low-resource languages while avoiding the excessive time cost of training all model parameters.
3Adaptability or versatility
If large-scale ASR models are tuned for low-resource languages, then speech recognition capability for low-resource languages is improved, but model size increases making deployment on mobile devices difficult
Solution Approach 1:
The pre-trained ASR model serves as a universal base that can recognize multiple languages through a single shared parameter set. By adding a small adaptation layer that can be configured for different languages, the same model infrastructure achieves multi-language capability without requiring separate large models for each language, thus maintaining compact size while improving adaptability.
Data Source
AI summary
A method includes obtaining an ASR model trained to recognize speech in a first language and receiving transcribed training utterances in a second language. The method also includes integrating the ASR model with an input reprogramming module and a latent reprogramming module. The method also includes adapting the ASR model to learn how to recognize speech in the second language by training the input reprogramming module and the latent reprogramming module while parameters of the ASR model are frozen.