Wav2Vec 2.0 embeddings for pathological speech correlate most with spectral (0.77) and prosodic (0.71) eGeMAPS features, especially the first MFCC coefficient across layers.
Prediction of speech impairment in patients treated for oral or oropharyngeal cancer using automatic speech analysis,
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What Does a Pathological Speech Assessment Model Know about Acoustic Features? A Case Study on Oral and Oropharyngeal Cancer Patients
Wav2Vec 2.0 embeddings for pathological speech correlate most with spectral (0.77) and prosodic (0.71) eGeMAPS features, especially the first MFCC coefficient across layers.