Toward Joint Language Modeling for Speech Units and Text
This work addresses the challenge of integrating speech and text for language modeling, which could benefit applications in spoken language understanding, but it appears incremental as it builds on existing tokenization and mixing techniques.
The paper tackles the problem of jointly modeling speech units and text, which has received little attention in language modeling, by exploring speech tokenizers and mixed data construction methods. The results show that the joint language model improves over a speech-only baseline on spoken language understanding tasks and demonstrates zero-shot cross-modal transferability.
Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare different speech tokenizers to transform continuous speech signals into discrete units and use different methods to construct mixed speech-text data. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. We also fine-tune the LM on downstream spoken language understanding (SLU) tasks with different modalities (speech or text) and test its performance to assess the model's learning of shared representations. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks and shows zero-shot cross-modal transferability.