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• W3193839640 abstract "Speech is known to serve as an early indicator ofneurological decline, particularly in motor diseases. There issignificant interest in developing automated, objective signalanalytics that detect clinically-relevant changes and in evaluatingthese algorithms against the existing gold-standard: perceptualevaluation by trained speech and language pathologists.Hypernasality, the result of poor control of the velopharyngealflap – the soft palate regulating airflow between the oral andnasal cavities – is one such speech symptom of interest, as precisevelopharyngeal control is difficult to achieve under neuromusculardisorders. However, a host of co-modulating variables givehypernasal speech a complex and highly variable acoustic signature,making it difficult for skilled clinicians to assess and forautomated systems to evaluate. Previous work in ratinghypernasality from speech relies on either engineered featuresbased on statistical signal processing or machine learning modelstrained end-to-end on clinical ratings of disordered speechexamples. Engineered features often fail to capture the complexacoustic patterns associated with hypernasality, while end-to-endmethods tend to overfit to the small datasets on which they aretrained. In this thesis, I present a set of acoustic features,models, and strategies for characterizing hypernasality indysarthric speech that split the difference between these twoapproaches, with the aim of capturing the complex perceptualcharacter of hypernasality without overfitting to the smalldatasets available. The features are based on acoustic modelstrained on a large corpus of healthy speech, integrating expertknowledge to capture known perceptual characteristics of hypernasalspeech. They are then used in relatively simple linear models topredict clinician hypernasality scores. These simple models arerobust, generalizing across diseases and outperformingcomprehensive set of baselines in accuracy and correlation. Thisnovel approach represents a new state-of-the-art in objectivehypernasality assessment." @default.
• W3193839640 created "2021-08-30" @default.
• W3193839640 creator A5034254782 @default.
• W3193839640 date "2020-01-01" @default.
• W3193839640 modified "2023-09-27" @default.
• W3193839640 title "Characterizing Dysarthric Speech with Transfer Learning" @default.
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