  Prior studies in the automatic classification of voice quality have mainly
studied the use of the acoustic speech signal as input. Recently, a few studies
have been carried out by jointly using both speech and neck surface
accelerometer (NSA) signals as inputs, and by extracting MFCCs and glottal
source features. This study examines simultaneously-recorded speech and NSA
signals in the classification of voice quality (breathy, modal, and pressed)
using features derived from three self-supervised pre-trained models
(wav2vec2-BASE, wav2vec2-LARGE, and HuBERT) and using a SVM as well as CNNs as
classifiers. Furthermore, the effectiveness of the pre-trained models is
compared in feature extraction between glottal source waveforms and raw signal
waveforms for both speech and NSA inputs. Using two signal processing methods
(quasi-closed phase (QCP) glottal inverse filtering and zero frequency
filtering (ZFF)), glottal source waveforms are estimated from both speech and
NSA signals. The study has three main goals: (1) to study whether features
derived from pre-trained models improve classification accuracy compared to
conventional features (spectrogram, mel-spectrogram, MFCCs, i-vector, and
x-vector), (2) to investigate which of the two modalities (speech vs. NSA) is
more effective in the classification task with pre-trained model-based
features, and (3) to evaluate whether the deep learning-based CNN classifier
can enhance the classification accuracy in comparison to the SVM classifier.
The results revealed that the use of the NSA input showed better classification
performance compared to the speech signal. Between the features, the
pre-trained model-based features showed better classification accuracies, both
for speech and NSA inputs compared to the conventional features. It was also
found that the HuBERT features performed better than the wav2vec2-BASE and
wav2vec2-LARGE features.
