Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

Bayerl SP, Wagner D, Nöth E, Riedhammer K (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: International Speech Communication Association

Book Volume: 2022-September

Pages Range: 2868-2872

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

Event location: Incheon KR

DOI: 10.21437/Interspeech.2022-10908

Abstract

Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. This paper shows that fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features for detecting stuttering in speech; both within and across languages. We evaluate our method on FluencyBank, [2] and the German therapy-centric Kassel State of Fluency (KSoF) [3] dataset by training Support Vector Machine classifiers using features extracted from the fine-tuned models for six different stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27% w.r.t. F1-score.

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:

Bayerl, S.P., Wagner, D., Nöth, E., & Riedhammer, K. (2022). Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 2868-2872). Incheon, KR: International Speech Communication Association.

MLA:

Bayerl, Sebastian P., et al. "Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0." Proceedings of the 23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, Incheon International Speech Communication Association, 2022. 2868-2872.

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