Detecting Vocal Fatigue with Neural Embeddings

  • Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three types of neural embeddings after 40 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation.

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Metadaten
Author:Sebastian P. BayerlORCiD, Dominik Wagner, Ilja BaumannORCiD, Tobias BockletORCiD, Korbinian RiedhammerORCiD
DOI:https://doi.org/10.1016/j.jvoice.2023.01.012
ISSN:0892-1997
Parent Title (English):Journal of Voice
Publisher:Elsevier BV
Document Type:Article
Language:English
Date of first Publication:2023/02/09
Reviewed:Begutachtet/Reviewed
Release Date:2024/08/01
Tag:Detection; Neural embeddings; Visualization; Vocal fatigue
Pagenumber:11
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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