Abstract:
Dysarthria is a motor speech disorder that occurs when the muscles involved in speech
production are weakened or not properly coordinated, often due to neurological conditions
such as stroke, cerebral palsy, or other diseases. Early detection is critical for
timely intervention and treatment planning to improve the quality of life for those experiencing
dysarthria. Traditional methods for assessing speech impairment, such as
subjective evaluations and conventional acoustic analyses, are often time-consuming,
biased, and less efficient. Advancements in machine learning, particularly deep learning
techniques like Convolutional Neural Networks (CNNs) and Long Short-Term Memory
(LSTM) networks, offer new opportunities for the automatic detection of dysarthria
based on speech signals. However, challenges such as high-dimensional data, overfitting,
and data scarcity remain. This thesis presents a novel deep learning model for the
automatic detection of dysarthria in speech data. The model combines a SincNet layer,
which uses band-pass filters based on the sinc function to extract audio features, with
CNN and LSTM layers to capture spatial and temporal dynamics in speech signals.
By integrating these components, the proposed model aims to learn features from raw
audio and effectively handle sequential data.
The study’s objectives include developing and evaluating the proposed model for
dysarthria detection, comparing its performance with existing models, and examining
factors contributing to its success. Additionally, the model’s robustness and generalization
capabilities are tested on publicly available TORGO datasets and achieved an
overall accuracy of 99% for binary classification and 98% training accuracy for multiclass
classification.
Potential applications of this research span various domains, including healthcare
for early detection and intervention, rehabilitation for personalized therapy programs,
and education to support students with speech difficulties. The thesis concludes with
recommendations for future research and practical applications of the findings in these
areas.
