Textually Pretrained Speech Language Models
Authors: name: Michael Hassid, name: Tal Remez, name: Tu Anh Nguyen, name: Itai Gat, name: Alexis Conneau, name: Felix Kreuk, name: Jade Copet, name: Alexandre Defossez, name: Gabriel Synnaeve, name: Emmanuel Dupoux, name: Roy Schwartz, name: Yossi Adi

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.
Speech samples can be found on our website:
https://pages.cs.huji.ac.il/adiyoss-lab/twist/ .