Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech
This work addresses the need for automated tools in medical diagnosis for dysarthria, though it is incremental as it applies an existing model to a specific domain.
The paper tackled the problem of automatically detecting and classifying the severity of dysarthria from speech signals using a pre-trained wav2vec 2.0 model as a feature extractor, achieving absolute accuracy improvements of 1.23% for detection and 10.62% for severity classification compared to baseline methods.
Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).