Large Language Models for Dysfluency Detection in Stuttered Speech

Wagner D, Bayerl SP, Baumann I, Riedhammer K, Nöth E, Bocklet T (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: International Speech Communication Association

Pages Range: 5118-5122

Conference Proceedings Title: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Event location: Kos Island GR

DOI: 10.21437/Interspeech.2024-2120

Abstract

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.

Authors with CRIS profile

Elmar Nöth Professur für Informatik (Mustererkennung)

Involved external institutions

Technische Hochschule Nürnberg "Georg Simon Ohm" DE Germany (DE)

How to cite

APA:

Wagner, D., Bayerl, S.P., Baumann, I., Riedhammer, K., Nöth, E., & Bocklet, T. (2024). Large Language Models for Dysfluency Detection in Stuttered Speech. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 5118-5122). Kos Island, GR: International Speech Communication Association.

MLA:

Wagner, Dominik, et al. "Large Language Models for Dysfluency Detection in Stuttered Speech." Proceedings of the 25th Interspeech Conferece 2024, Kos Island International Speech Communication Association, 2024. 5118-5122.

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