Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques.

Area of Science:

• Speech Pathology
• Artificial Intelligence
• Machine Learning

Background:

• Accurate dysarthria severity assessment is crucial for patient monitoring, therapy planning, and developing automatic speech recognition systems.
• Various deep learning models and acoustic features can be employed for classifying dysarthria severity.

Purpose of the Study:

• To comparatively evaluate different deep learning techniques and acoustic features for classifying dysarthria severity.
• To investigate the effectiveness of basic speech features, disorder-specific features, and low-dimensional feature representations.

Main Methods:

• Evaluated deep neural networks (DNN), convolutional neural networks (CNN), gated recurrent units (GRU), and long short-term memory (LSTM) networks.
• Utilized Mel-frequency cepstral coefficients (MFCCs) and constant-Q cepstral coefficients (CQCCs) as basic speech features.
• Incorporated speech-disorder specific features (prosody, articulation, phonation, glottal) and low-dimensional i-vectors derived from subspace modeling.
• Tested models on UA-Speech and TORGO databases.

Main Results:

• The DNN classifier using MFCC-based i-vectors achieved the highest accuracy: 93.97% (speaker-dependent) and 49.22% (speaker-independent) on the UA-Speech database.
• Performance varied across different deep learning architectures and feature sets.
• Disorder-specific features and i-vectors showed promise in improving classification accuracy.

Conclusions:

• Deep learning models, particularly DNNs with MFCC-based i-vectors, demonstrate strong potential for accurate dysarthria severity classification.
• The findings support the use of advanced acoustic feature extraction and deep learning for improving dysarthric speech analysis.
• This research contributes to the development of more effective tools for speech pathologists and automatic dysarthric speech recognition systems.