Accurately detecting dysfluencies in spoken language can help to improve the
performance of automatic speech and language processing components and support
the development of more inclusive speech and language technologies. Inspired by
the recent trend towards the deployment of large language models (LLMs) as
universal learners and processors of non-lexical inputs, such as audio and
video, we approach the task of multi-label dysfluency detection as a language
modeling problem. We present hypotheses candidates generated with an automatic
speech recognition system and acoustic representations extracted from an audio
encoder model to an LLM, and finetune the system to predict dysfluency labels
on three datasets containing English and German stuttered speech. The
experimental results show that our system effectively combines acoustic and
lexical information and achieves competitive results on the multi-label
stuttering detection task.

通过将自动语音识别系统生成的假设候选项和从音频编码模型提取的声学表示输入到大型语言模型（LLMs）中，我们将多标签异味检测任务作为一种语言建模问题进行了研究，并在包含英语和德语结巴语音的三个数据集上对系统进行了优化，以预测异味标签，实验结果表明我们的系统有效地结合声学和词法信息，在多标签结巴检测任务上取得了有竞争力的结果。