Speech is known to serve as an early indicator of neurological decline, particularly in motor diseases. There is significant interest in developing automated, objective signal analytics that detect clinically-relevant changes and in evaluating these algorithms against the existing gold-standard: perceptual evaluation by trained speech and language pathologists. Hypernasality, the result of poor control of the velopharyngeal flap---the soft palate regulating airflow between the oral and nasal cavities---is one such speech symptom of interest, as precise velopharyngeal control is difficult to achieve under neuromuscular disorders. However, a host of co-modulating variables give hypernasal speech a complex and highly variable acoustic signature, making it difficult for skilled clinicians to assess and for automated systems to evaluate. Previous work in rating hypernasality from speech relies on either engineered features based on statistical signal processing or machine learning models trained end-to-end on clinical ratings of disordered speech examples. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, while end-to-end methods tend to overfit to the small datasets on which they are trained. In this thesis, I present a set of acoustic features, models, and strategies for characterizing hypernasality in dysarthric speech that split the difference between these two approaches, with the aim of capturing the complex perceptual character of hypernasality without overfitting to the small datasets available. The features are based on acoustic models trained on a large corpus of healthy speech, integrating expert knowledge to capture known perceptual characteristics of hypernasal speech. They are then used in relatively simple linear models to predict clinician hypernasality scores. These simple models are robust, generalizing across diseases and outperforming comprehensive set of baselines in accuracy and correlation. This novel approach represents a new state-of-the-art in objective hypernasality assessment.

Speech nasality disorders are characterized by abnormal resonance in the nasal cavity. Hypernasal speech is of particular interest, characterized by an inability to prevent improper nasalization of vowels, and poor articulation of plosive and fricative consonants, and can lead to negative communicative and social consequences. It can be associated with a range of conditions, including cleft lip or palate, velopharyngeal dysfunction (a physical or neurological defective closure of the soft palate that regulates resonance between the oral and nasal cavity), dysarthria, or hearing impairment, and can also be an early indicator of developing neurological disorders such as ALS. Hypernasality is typically scored perceptually by a Speech Language Pathologist (SLP). Misdiagnosis could lead to inadequate treatment plans and poor treatment outcomes for a patient. Also, for some applications, particularly screening for early neurological disorders, the use of an SLP is not practical. Hence this work demonstrates a data-driven approach to objective assessment of hypernasality, through the use of Goodness of Pronunciation features. These features capture the overall precision of articulation of speaker on a phoneme-by-phoneme basis, allowing demonstrated models to achieve a Pearson correlation coefficient of 0.88 on low-nasality speakers, the population of most interest for this sort of technique. These results are comparable to milestone methods in this domain.