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.
