  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 kinds of neural embeddings after only 50 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.
