@inproceedings{held-etal-2025-distilling,
    title = "Distilling an End-to-End Voice Assistant Without Instruction Training Data",
    author = "Held, William Barr  and
      Zhang, Yanzhe  and
      Shi, Weiyan  and
      Li, Minzhi  and
      Ryan, Michael J  and
      Yang, Diyi",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.388/",
    pages = "7876--7891",
    ISBN = "979-8-89176-251-0",
    abstract = "Voice assistants, such as Siri and Google Assistant, typically model audio and text separately, resulting in lost speech information and increased complexity. Recent efforts to address this with end-to-end Speech Large Language Models (speech-in, text-out) trained with supervised finetuning (SFT) have led to models ``forgetting'' capabilities from text-only LLMs. Our work proposes an alternative paradigm for training Speech LLMs without instruction data, using the response of a text-only LLM to transcripts as self-supervision. Importantly, this process can be performed without annotated responses. We show that our Distilled Voice Assistant (DiVA) generalizes to Spoken Question Answering, Classification, and Translation. Furthermore, DiVA better matches user preferences, achieving a 72{\%} win rate compared with state-of-the-art models like Qwen 2 Audio, despite using $>$100x less training compute."
}