5 accepted papers
Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing…
The advent of Deep-Learning techniques and the increasing importance of personalization in voice assistants fueled the need for open vocabulary keyword detection systems, in which, the user can enroll a keyword using audio or text as a modality. A text enrollment-based custom-keyword detection syste…
Accurate detection of epoch locations is important in extracting the features from the speech signal for automatic detection and assessment of voice disorders. Therefore, this study aimed to compare the various algorithms for detecting epoch locations from the speech associated with voice disorders.…
Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing methods explore the short-term spectral infor…
This paper proposes a new speech feature representation that improves the intelligibility assessment of dysarthric speech. The formulation of the feature set is motivated from the human auditory perception and high time-frequency resolution property of single frequency filtering (SFF) technique. The…