Exploring Pathological Speech Quality Assessment with ASR-Powered
  Wav2Vec2 in Data-Scarce Context
Tuan Nguyen Corinne Fredouille Alain Ghio Mathieu Balaguer Virginie Woisard
Automatic speech quality assessment has raised more attention as an
alternative or support to traditional perceptual clinical evaluation. However,
most research so far only gains good results on simple tasks such as binary
classification, largely due to data scarcity. To deal with this challenge,
current works tend to segment patients' audio files into many samples to
augment the datasets. Nevertheless, this approach has limitations, as it
indirectly relates overall audio scores to individual segments. This paper
introduces a novel approach where the system learns at the audio level instead
of segments despite data scarcity. This paper proposes to use the pre-trained
Wav2Vec2 architecture for both SSL, and ASR as feature extractor in speech
assessment. Carried out on the HNC dataset, our ASR-driven approach established
a new baseline compared with other approaches, obtaining average $MSE=0.73$ and
$MSE=1.15$ for the prediction of intelligibility and severity scores
respectively, using only 95 training samples. It shows that the ASR based
Wav2Vec2 model brings the best results and may indicate a strong correlation
between ASR and speech quality assessment. We also measure its ability on
variable segment durations and speech content, exploring factors influencing
its decision.