Large Language Models for Dysfluency Detection in Stuttered Speech

  • 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.

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Author:Dominik Wagner, Sebastian P. BayerlORCiD, Ilja BaumannORCiD, Korbinian RiedhammerORCiD, Elmar Nöth, Tobias BockletORCiD
DOI:https://doi.org/10.48550/arXiv.2406.11025
ArXiv Id:http://arxiv.org/abs/arXiv:2406.11025v1
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2024/06/30
Release Date:2024/10/17
Tag:dysfluency detection, stuttering, large language models, wav2vec 2.0, Whisper, pathological speech
Pagenumber:6
Konferenzangabe:Interspeech 2024
institutes:Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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