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200947 https://hdl.handle.net/2286/R.2.N.200947 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 2027-05-01T13:03:12 153 pages Doctoral Dissertation Academic theses en Xiong, Yan Chakrabarti, Chaitali Berisha, Visar Liss, Julie Alkhateeb, Ahmed Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Electrical Engineering The rapid development of deep learning (DL) techniques has revolutionized the field of speech processing, enabling models to extract relevant features from speech and accomplish various speech-processing tasks. This thesis explores two areas of DL-based speech processing: First, development of novel neural networks for overlapping and noisy keyword spotting and voice activity detection. Second, using DL models in speech-based dysarthria detection and dysarthric wake-up word spotting. Keyword spotting (KWS) and voice activity detection (VAD) play an important role in speech-based human-computer interaction systems, enabling robust and efficient recognition of target phrases. However, traditional KWS models struggle with overlapped keyword spotting and detection in noisy environments. To address these problems, ResCap, a novel network integrating convolutional neural networks (CNNs) with capsule networks, is designed. ResCap uses capsule layers and dynamic routing to preserve spatial hierarchies in speech features. Evaluation results demonstrate that ResCap outperforms conventional KWS systems, particularly under train-test mismatched noise conditions. Furthermore, an extended version, ResCap+, incorporates long short-term memory (LSTM) networks to capture temporal dependencies, making it highly effective for VAD. When implemented on the Transmuter, ResCap+ significantly reduces computational complexity and energy consumption, making it suitable for real-time, low-power applications. The second topic of the thesis is a DL-based approach to acoustic clinical analysis, focusing on speech-based dysarthric detection and wake-up word spotting for dysarthric speakers. Dysarthria is a motor speech disorder characterized by impaired speech capability, which affects keyword spotting due to variability in speech patterns and reduced clarity of spoken keywords. To mitigate the performance loss, a low-resource wake-up word spotting system tailored for dysarthric speech is developed using contrastive learning and speaker-dependent tuning. Speech-based dysarthria detection focuses on predicting dysarthria using speech recordings. To achieve robust and high-performing DL-based dysarthria detection, a multi-task learning (MTL) framework with task-specific gradient projection (TGP) is introduced to mitigate overfitting and enhance generalization. Additionally, to better utilize the large-scale pre-trained speech foundation model in speech-based dysarthria detection, a TGP-MTL framework is implemented to mitigate the model overfitting and achieve higher dysarthria detection accuracy under in-corpus and cross-corpus situations. Electrical Engineering Computer Science Clinical Analysis Deep learning Dysarthria speech processing Deep Learning based Speech Processing and Acoustic Clinical Analysis