While the LAST approach presents significant advancements in speech tokenization, it also has potential limitations that could be addressed for further improvement:
Computational Resource Requirements: LAST requires more computational resources compared to traditional k-means tokenization methods due to the integration of a frozen text LM and additional adapter layers. To mitigate this, model distillation techniques could be employed to create a smaller, more efficient version of the LAST model that retains performance while reducing resource consumption.
Dependency on Pre-trained Models: The performance of LAST heavily relies on the quality of the pre-trained text LM and speech encoder. If these models are not well-suited for the target language or domain, the tokenization may suffer. Continuous updates and fine-tuning of these models on domain-specific data could enhance their adaptability and performance.
Risk of Token Collapse: The optimization process in LAST may lead to token collapse, where the model learns to produce a limited set of tokens. To address this, more robust regularization techniques could be implemented, such as dropout or noise injection during training, to encourage diversity in the generated tokens.
Limited Evaluation Metrics: The current evaluation metrics primarily focus on zero-resource speech modeling and ASR performance. Expanding the evaluation framework to include metrics for other speech-related tasks, such as emotion recognition and speaker identification, would provide a more comprehensive assessment of the model's capabilities.
Integration with Other Modalities: LAST currently focuses on speech and text modalities. Future work could explore the integration of visual or contextual information, enabling the model to leverage multimodal inputs for improved performance in tasks like speech emotion conversion or speech-to-speech translation.