Evidence for planning and motor subtypes of stuttering based on resting state functional connectivity
Rowe, H.P., Tourville, J.A., Nieto-Castanon, A., Garnett, E.O., Chow, H.M., Chang, S.-E., & Guenther, F.H. (2024). Brain and Language, 253, 105417.
We tested the hypothesis, generated from the Gradient Order Directions Into Velocities of Articulators (GODIVA) model, that adults who stutter (AWS) may comprise subtypes based on differing connectivity within the cortico-basal ganglia planning or motor loop. Based on resting state functional connectivity data from 91 AWS and 79 controls, we found two connections that accounted for most of the connectivity variability in AWS: left thalamus – left posterior inferior frontal sulcus (planning loop component) and left supplementary motor area – left ventral premotor cortex (motor loop component). A k-means clustering algorithm using the two connections revealed three clusters of AWS. Cluster 1 was significantly different from controls in both connections; Cluster 2 was significantly different in only the planning loop; and Cluster 3 was significantly different in only the motor loop. These findings suggest the presence of planning and motor subtypes of stuttering.
The efficacy of acoustic-based articulatory phenotyping for characterizing and classifying divergent neurodegenerative diseases
Rowe, H.P., Gochyyev, P., Lammert, A.C., Lowit, A., Spencer, K.A., Dickerson, B.C., Berry, J., & Green, J.R. (2022). Journal of Neural Transmission, 129, 1487-1511.
This study sought to assess the articulatory phenotypes of four neurodegenerative populations known to have divergent speech motor deficits and determine the efficacy of articulatory phenotyping for classifying different diseases. We aimed to address a primary limitation of the current differential diagnosis literature by expanding the range of neurological/pathophysiological deficits and articulatory characteristics examined in one study. We found evidence of distinct articulatory phenotypes for the four clinical groups (i.e., ALS, PA, PD, and nfPPA + PAOS), which highlights the phenotypic variability present across neurodegenerative diseases. Additionally, the phenotypes demonstrated strong classification accuracy for characterizing neurodegenerative diseases, which emphasizes the potential clinical utility of using a comprehensive profile of articulation.
Quantifying articulatory impairments in neurodegenerative motor diseases: A scoping review and meta-analysis of interpretable acoustic features
Rowe, H.P., Shellikeri, S., Yunusova, Y., Chenausky, K., & Green, J.R. (2022). International Journal of Speech-Language Pathology, 25(4), 486-499.
Overall, our findings revealed a strong focus in the speech motor literature on acoustic features that represent precision and an underrepresentation of studies on features that represent coordination, consistency, speed, and repetition rate. In light of the need for research across all articulatory components to elucidate articulatory phenotypes, the restricted focus on precision is problematic. Furthermore, while the limited data in our meta-analysis precluded us from making specific recommendations regarding the most promising feature for each population, our results revealed phenotypic variability in articulatory impairments across speech motor subtypes. This finding motivates the need to employ more impairment-specific knowledge in algorithm development, which may significantly extend the impact such models have for individuals with NMDs. However, there remains a significant need to broaden and deepen our understanding of the articulatory phenotypes underlying NMDs.
Characterizing dysarthria diversity for automatic speech recognition: A clinical perspective
Rowe, H.P., Gutz, S.E., Maffei, M.F., Tomanek, K., & Green, J.R. (2022). Frontiers in Computer Science (Perspectives inHuman-Media Interaction), 4, 1-8.
Improving ASR accuracy for dysarthric speech may have significant implications for communication and quality of life. This article outlined the sources of diversity inherent to speech motor disorders, their potential impact on ASR performance, and the importance of their representation in training sets. Representing dysarthric speech variability in ASR corpora may be an important step for improving disordered speech ASR and is consistent with the call to action in the artificial intelligence community to reduce bias in the training data by increasing diversity.
Speech as a biomarker: Opportunities, interpretability, and challenges
Ramanarayanan, V., Lammert, A.C., Rowe, H.P., Quatieri, T.F., & Green, J.R. (2022). ASHA Perspectives in Speech Science, 7, 276-283.
Over the past decade, the signal processing and machine learning literature has demonstrated notable advancements in automated speech processing with the use of artificial intelligence for medical assessment and monitoring (e.g., depression, dementia, and Parkinson’s disease, among others). Meanwhile, the clinical speech literature has identified several interpretable, theoretically motivated measures that are sensitive to abnormalities in the cognitive, linguistic, affective, motoric, and anatomical domains. Both fields have, thus, independently demonstrated the potential for speech to serve as an informative biomarker for detecting different psychiatric and physiological conditions. However, despite these parallel advancements, automated speech biomarkers have not been integrated into routine clinical practice to date. In this article, we present opportunities and challenges for adoption of speech as a biomarker in clinical practice and research. Toward clinical acceptance and adoption of speech-based digital biomarkers, we argue for the importance of several factors such as robustness, specificity, diversity, and physiological interpretability of speech analytics in clinical applications.
Validation of an acoustic-based framework of speech motor control: Assessing criterion and construct validity using kinematic and perceptual measures
Rowe, H.P., Stipancic, K.L., Lammert, A.C., & Green, J.R. (2021). Journal of Speech, Language, and Hearing Research, 64(12), 4718-4735.
The results of this study demonstrate that our acoustically driven framework has potential as an objective, valid, and clinically useful tool for profiling articulatory deficits in individuals with speech motor disorders. Our findings also suggest that compared to clinician ratings, instrumental measures may be more sensitive to subtle differences in articulatory function. With further research, this framework could provide accurate and reliable characterizations of articulatory impairment, which may eventually increase the efficacy of diagnosis and treatment for patients with different articulatory phenotypes.
