Toward Joint Language Modeling for Speech Units and Text

TL;DR

This paper explores joint language modeling for speech units and text, comparing tokenization methods, proposing evaluation metrics, and demonstrating improved SLU performance and cross-modal transferability.

Contribution

It introduces methods for joint modeling of speech and text, including tokenization, data mixing techniques, and evaluation metrics, advancing multimodal language understanding.

Findings

Joint LM improves SLU task performance over speech-only models.

Proposed mixing techniques enable zero-shot cross-modal transfer.

Automatic metrics effectively evaluate speech-text integration.

Abstract

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.