@inproceedings{yu-etal-2025-eliciting,
    title = "Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech",
    author = "Yu, Jianxing  and
      Zihao, Gou  and
      Li, Chen  and
      Wang, Zhisheng  and
      Yang, Peiji  and
      Chen, Wenqing  and
      Yin, Jian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.182/",
    pages = "3679--3695",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper focuses on generating speech with the acoustic style that meets users' needs based on their open-domain instructions. To control the style, early work mostly relies on pre-defined rules or templates. The control types and formats are fixed in a closed domain, making it hard to meet diverse needs of users. One solution is to resort to instructions in free text to guide the generation. Current work mainly studies the instructions that clearly specify the acoustic styles, such as low pitch and fast speed. However, the instructions are complex, some even vague and abstract, such as ``Generate a voice of a woman who is heartbroken due to a breakup. It is hard to infer this implicit style by traditional matching-based methods. To address this problem, we propose a new controllable model. It first utilizes multimodal LLMs with knowledge-augmented techniques to infer the desired speech style from the instructions. The powerful language understanding ability of LLMs can help us better elicit the implicit style factors from the instruction. By using these factors as a control condition, we design a diffusion-based generator adept at finely adjusting speech details. That offers higher flexibility to meet complex users' needs. Next, we verify the output speech from three aspects, i.e., consistency of decoding state, mel-spectrogram, and instruction style. This verified feedback can inversely optimize the generator. Extensive experiments are conducted on three popular datasets. The results show the effectiveness and good controllability of our approach."
}