Textually Pretrained Speech Language Models
This work addresses the challenge of building better-performing speech language models for applications in speech processing, though it appears incremental as it builds on existing textual models.
The paper tackles the problem of training speech language models (SpeechLMs) without textual supervision by proposing TWIST, a method that uses a warm-start from pretrained textual language models, resulting in outperformance over cold-start SpeechLMs in automatic and human evaluations.
Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. We make speech samples, code and models publicly available: https://pages.cs.huji.ac.il/adiyoss-lab/twist/ .