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Vocal fatigue refers to the feeling of tiredness and weakness of voice due toextended utilization. This paper investigates the effectiveness of neuralembeddings 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 captureinformation about the change in vocal characteristics of a speaker duringprolonged voice usage. We show that vocal fatigue can be reliably predictedusing all three kinds of neural embeddings after only 50 minutes of continuousspeaking when temporal smoothing and normalization are applied to the extractedembeddings. We employ support vector machines for classification and achieveaccuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy scoreof 76%, when the trained system is applied to a different speaker and recordingenvironment without any adaptation.

Detecting Vocal Fatigue with Neural Embeddings