The LAST (Language Model Aware Speech Tokenization) method can be extended to handle multiple languages or multilingual settings by leveraging the capabilities of pre-trained text language models (LMs) that are already designed for multilingual tasks. Here are several strategies to achieve this:
Multilingual Pre-trained Text LMs: Utilize multilingual LMs, such as mBERT or XLM-R, which are trained on diverse language datasets. By integrating these models into the LAST framework, the speech tokenizer can learn to generate tokens that are language-agnostic, allowing for better performance across different languages.
Language-Specific Adaptation Layers: Introduce language-specific adapter layers within the LAST architecture. These layers can be fine-tuned on language-specific datasets, enabling the model to adapt its tokenization process based on the linguistic characteristics of each language while still benefiting from the shared knowledge of the frozen multilingual LM.
Dynamic Tokenization: Implement a dynamic tokenization strategy where the model can switch between different tokenization schemes based on the detected language of the input speech. This could involve training separate quantization models for each language while maintaining a shared backbone for the speech encoder.
Cross-Lingual Transfer Learning: Leverage cross-lingual transfer learning techniques, where knowledge from high-resource languages can be transferred to low-resource languages. This can enhance the performance of the LAST method in multilingual settings, especially for languages with limited training data.
Evaluation on Multilingual Benchmarks: To ensure the effectiveness of the extended LAST method, it should be evaluated on multilingual benchmarks and tasks, such as multilingual ASR and speech translation, to assess its performance across various languages and dialects.