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Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care., Competing Interests: Declaration of competing interest All authors are full-time employees of Modality.AI and hold stock options in the company. Modality did not influence or restrict the submission of this publication., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: There exists a high burden of oral diseases. Yet, the utilization of oral health care remains low. Understanding how, when, and why/why not individuals utilize dental healthcare resources is essential for planning health services and developing policies as it reflects the population's oral health needs, helps allocate resources efficiently, and formulate policies that are tailored to address their needs., Objectives: To assess the utilization of dental health services and its associated factors along with barriers and facilitators among adults residing in Ernakulam district, Kerala, using the Anderson healthcare model for healthcare utilization., Methods: A mixed-method study was conducted among adults aged 18 years and above in urban and rural wards of Ernakulam district using the cluster sampling method. Univariate and multivariate analysis were used for the quantitative part, and thematic analysis was used for the qualitative aspect. The total sample size was 544., Results: The dental healthcare utilization was 15.4 ± 2.9 % among the study participants. Age and education were associated with dental healthcare utilization. The level of education, pain, and self-consciousness of oral diseases were independent predictors. Barriers identified were negative attitudes, financial restraints, and difficulty in access, while facilitators were trust in service providers, availability of services, and a positive attitude., Conclusion: Utilization was poor despite the perceived need for oral health care. This was affected by several personal and system-level facilitators and barriers., Competing Interests: There are no conflicts of interest., (© 2024 The Authors.)

How the speech production and perception systems evolved in humans still remains a mystery today. Previous research suggests that human auditory systems are able, and have possibly evolved, to preserve maximal information about the speaker's articulatory gestures. This paper attempts an initial step towards answering the complementary question of whether speakers' articulatory mechanisms have also evolved to produce sounds that can be optimally discriminated by the listener's auditory system. To this end we explicitly model, using computational methods, the extent to which derived representations of "primitive movements" of speech articulation can be used to discriminate between broad phone categories. We extract interpretable spatio-temporal primitive movements as recurring patterns in a data matrix of human speech articulation, i.e. representing the trajectories of vocal tract articulators over time. To this end, we propose a weakly-supervised learning method that attempts to find a part-based representation of the data in terms of recurring basis trajectory units (or primitives) and their corresponding activations over time. For each phone interval, we then derive a feature representation that captures the co-occurrences between the activations of the various bases over different time-lags. We show that this feature, derived entirely from activations of these primitive movements, is able to achieve a greater discrimination relative to using conventional features on an interval-based phone classification task. We discuss the implications of these findings in furthering our understanding of speech signal representations and the links between speech production and perception systems.

We propose a practical , feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks.