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Pokorny, Florian B. [Verfasser:in]; Linke, Julian [Verfasser:in]; Seddiki, Nico [Verfasser:in]; Lohrmann, Simon [Verfasser:in]; Gerstenberger, Claus [Verfasser:in]; Haspl, Katja [Verfasser:in]; Feiner, Marlies [Verfasser:in]; Eyben, Florian [Verfasser:in]; Hagmüller, Martin [Verfasser:in]; Schuppler, Barbara [Verfasser:in]; Kubin, Gernot [Verfasser:in]; Gugatschka, Markus [Verfasser:in]

VocDoc, what happened to my voice? Towards automatically capturing vocal fatigue in the wild

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  • Medientyp: E-Artikel
  • Titel: VocDoc, what happened to my voice? Towards automatically capturing vocal fatigue in the wild
  • Beteiligte: Pokorny, Florian B. [Verfasser:in]; Linke, Julian [Verfasser:in]; Seddiki, Nico [Verfasser:in]; Lohrmann, Simon [Verfasser:in]; Gerstenberger, Claus [Verfasser:in]; Haspl, Katja [Verfasser:in]; Feiner, Marlies [Verfasser:in]; Eyben, Florian [Verfasser:in]; Hagmüller, Martin [Verfasser:in]; Schuppler, Barbara [Verfasser:in]; Kubin, Gernot [Verfasser:in]; Gugatschka, Markus [Verfasser:in]
  • Erschienen: Augsburg University Publication Server (OPUS), 2023-10-25
  • Sprache: Englisch
  • DOI: https://doi.org/10.1016/j.bspc.2023.105595
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: Objective: Voice problems that arise during everyday vocal use can hardly be captured by standard outpatient voice assessments. In preparation for a digital health application to automatically assess longitudinal voice data ‘in the wild’ – the VocDoc, the aim of this paper was to study vocal fatigue from the speaker’s perspective, the healthcare professional’s perspective, and the ‘machine’s’ perspective. Methods: We collected data of four voice healthy speakers completing a 90-min reading task. Every 10 min the speakers were asked about subjective voice characteristics. Then, we elaborated on the task of elapsed speaking time recognition: We carried out listening experiments with speech and language therapists and employed random forests on the basis of extracted acoustic features. We validated our models speaker-dependently and speaker-independently and analysed underlying feature importances. For an additional, clinical application-oriented scenario, we extended our dataset for lecture recordings of another two speakers. Results: Self- and expert-assessments were not consistent. With mean F1 scores up to 0.78, automatic elapsed speaking time recognition worked reliably in the speaker-dependent scenario only. A small set of acoustic features – other than features previously reported to reflect vocal fatigue – was found to universally describe long-term variations of the voice. Conclusion: Vocal fatigue seems to have individual effects across different speakers. Machine learning has the potential to automatically detect and characterise vocal changes over time. Significance: Our study provides technical underpinnings for a future mobile solution to objectively capture pathological long-term voice variations in everyday life settings and make them clinically accessible.
  • Zugangsstatus: Freier Zugang