Vocal Markers from Sustained Phonation in Huntington’s Disease

Abstract

Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntington's Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers. We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up. We used two sets of features: Phonatory features and Modulation Power Spectrum Features. We found that phonation is not sufficient for the identification of sub-clinical disorders of premanifest gene carriers. According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntington's Disease.

Systematic Review of Machine Learning Approaches for Detecting Developmental Stuttering

“…Given that model architectures and feature inputs vary dramatically across studies, it would be desirable to have openly-available source code of a standard model available as has been done for Huntington's disease [57] where results for the reference data set are known. A logistic regression with specified MFCC features as inputs and fluent/stuttered as outcomes is one possible standard model.…”

Managing, storing, and sharing long-form recordings and their annotations

“…First, there is a line of research on interaction and its effects on well-being among neurotypical adults (e.g., Mehl et al (2001)). Second, audio data from wearables holds promise for individuals with medical and psychological conditions that have behavioral consequences which can evolve over time, including conditions that lead to coughing (Wu et al, 2018) and/or neurogenerative disorders (Riad et al, 2020). Third, some researchers hope to gather datasets on child development combining multiple information sources, such as parental reports, as well as other sensors picking up motion and psychophysiological data, with the goal of potentially intervening when it is needed (Levin et al, 2021).…”

“…Figure 1 summarizes which data are typically found in corpora of long-form recordings used for child language acquisition studies, while Figure 2 illustrates annotations drawn from a public corpus. More recently, long-form data is also being discussed in the context of neurological disorders (e.g., Riad et al 2020). In this article, we define the unique space of difficulties surrounding long-form recordings, and introduce a set of packages that provides practical solutions, with a focus on child-centered recordings.…”

Managing, storing, and sharing long-form recordings and their annotations

“…First, there is a line of research on interaction and its effects on well-being among neurotypical adults (e.g., Mehl et al (2001)). Second, audio data from wearables holds promise for individuals with medical and psychological conditions that have behavioral consequences which can evolve over time, including conditions that lead to coughing (Wu et al, 2018) and/or neurogenerative disorders (Riad et al, 2020). Third, some researchers hope to gather datasets on child development combining multiple information sources, such as parental reports, VanDam, 2015) are compared to the VTC's.…”

“…The LENA Foundation created a hardware-software combination that illuminated the potential of this technique for theoretical and applied purposes (e.g., Christakis et al 2009;Warlaumont et al 2014). More recently, long-form data is being discussed in the context of neurological disorders (e.g., Riad et al 2020). In this article, we define the unique space of difficulties surrounding long-form recordings, and introduce a set of packages that provides practical solutions, with a focus on child-centered recordings.…”