Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem

Sebastian P. Bayerl; Dominik Wagner; Florian Hönig; Tobias Bocklet; Elmar Nöth; Korbinian Riedhammer

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arXiv · 2210.15982

Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem

Sebastian P. Bayerl·Dominik Wagner·Florian Hönig·Tobias Bocklet·Elmar Nöth·Korbinian Riedhammer

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Abstract

Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or viewed detecting each dysfluency type as an isolated task; that does not capture the nature of stuttering, where one dysfluency seldom comes alone, i.e., co-occurs with others. This work explores an approach based on a modified wav2vec 2.0 system for end-to-end stuttering detection and classification as a multi-label problem. The method is evaluated on combinations of three datasets containing English and German stuttered speech, yielding state-of-the-art results for stuttering detection on the SEP-28k-Extended dataset. Experimental results provide evidence for the transferability of features and the generalizability of the method across datasets and languages.

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Sebastian P. Bayerl, Dominik Wagner, Florian Hönig, Tobias Bocklet, Elmar Nöth, Korbinian Riedhammer. 2022-10-28. Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem. https://arxiv.org/abs/2210.15982

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