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/ .

翻译：语音语言模型仅处理并生成声学数据，无需文本监督。在本工作中，我们提出TWIST方法，该方法通过从预训练的文本语言模型进行热启动来训练语音语言模型。通过自动评估和人工评估，我们证明TWIST在所有指标上均优于冷启动语音语言模型。我们实证分析了不同模型设计选择（如语音分词器、预训练文本模型及数据集规模）的影响。研究发现，模型规模与数据集规模对于构建性能更优的语音语言模型均起到关键作用。基于实验观察，我们提出了据我们所知规模最大的语音语言模型（就参数量和训练数据量而言）。此外，我们引入了两个口语版本的StoryCloze文本基准测试，以进一步改进模型评估并推动该领域的未来研究。语音样本可在我们的网站获取：https://pages.cs.huji.ac.il/adiyoss-lab/twist/ 。