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Recommendations for common outcome measures following pediatric traumatic brain injury (TBI) support the integration of instrumental measurements alongside perceptual assessment in recovery and treatment plans. A comprehensive set of sensitive, robust and non-invasive measurements is therefore essential in assessing variations in speech characteristics over time following pediatric TBI. In this article, we study the changes in the acoustic speech patterns of a pediatric cohort of ten subjects diagnosed with severe TBI. We extract a diverse set of both well-known and novel acoustic features from child speech recorded throughout the year after the child produced intelligible words. These features are analyzed individually and by speech subsystem, within-subject and across the cohort. As a group, older children exhibit highly significant (p<0.01) increases in pitch variation and phoneme diversity, shortened pause length, and steadying articulation rate variability. Younger children exhibit similar steadied rate variability alongside an increase in formant-based articulation complexity. Correlation analysis of the feature set with age and comparisons to normative developmental data confirm that age at injury plays a significant role in framing the recovery trajectory. Nearly all speech features significantly change (p<0.05) for the cohort as a whole, confirming that acoustic measures supplementing perceptual assessment are needed to identify efficacious treatment targets for speech therapy following TBI.

We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices". We also identify the datasets needed to support these research areas. Based on the review and our research experience in the multi-device domain, we conclude with an outlook on the future evolution, Comment: Accepted for INTERSPEECH 2022

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial Y. pestis exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

Many individuals struggle to understand speech in listening scenarios that includereverberation and background noise. An individual’s ability to understand speech arisesfrom a combination of peripheral auditory function, central auditory function, and generalcognitive abilities. The interaction of these factors complicates the prescription oftreatment or therapy to improve hearing function. Damage tothe auditory peripherycan be studied in animals; however, this method alone is not enough to understandthe impact of hearing loss on speech perception. Computational auditory models bridgethe gap between animal studies and human speech perception.Perturbations to themodeled auditory systems can permit mechanism-based investigations into observedhuman behavior. In this study, we propose a computational model that accounts forthe complex interactions between different hearing damagemechanisms and simulateshuman speech-in-noise perception. The model performs a digit classification task asa human would, with only acoustic sound pressure as input. Thus, we can use themodel’s performance as a proxy for human performance. This two-stage model consistsof a biophysical cochlear-nerve spike generator followed by a deep neural network(DNN) classifier. We hypothesize that sudden damage to the periphery affects speechperception and that central nervous system adaptation overtime may compensatefor peripheral hearing damage. Our model achieved human-like performance acrosssignal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving50% digit recognition accuracy at−20.7 dB SNR. Results were comparable to eightNH participants on the same task who achieved 50% behavioralperformance at−22dB SNR. We also simulated medial olivocochlear reflex (MOCR)and auditory nervefiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs comparedto higher SNRs. Our simulated performance following ANF loss is consistent withthe hypothesis that cochlear synaptopathy im, United States. Department of Defense. Research and Engineering (Air Force Contract No. FA8702-15-D-0001), National Institutes of Health (U.S.) (T32 Trainee Grant No. 5T32DC000038-27), National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant DGE1745303)

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 161-166)., Early signs of patient deterioration have been documented in the medical literature. Recognition of such signs offers the possibility of treatment with sufficient lead time to prevent irreversible organ damage and death. Pediatric hospitals currently utilize simple, human evaluated rubrics called early warning scores to detect early signs of patient deterioration. These scores comprise subjective (patient behavior, clinician's impression) and objective (vital signs) components to assess patient health and are computed intermittently by the nursing staff. At Boston Children's Hospital (BCH), early warning scores are evaluated at least every four hours for each patient. Many hospitals monitor inpatients continuously to alert caregivers to changes in physiological status. At BCH, each hospital bed is equipped with a bedside monitor that continuously collects and archives vital sign data, such as heart rate, respiration rate, and arterial oxygen saturation. Continuous access to these physiological variables allows for the definition of a continuously evaluated early warning score on a reduced rubric. This thesis quantitatively assesses the performance of BCH's current Children's Hospital Early Warning Score (CHEWS). We also apply several standard machine learning approaches to investigate the utility of automatically collected bedside monitoring trend data for prediction of patient deterioration. Our results suggest that CHEWS offers at least a 6-hour warning with sensitivity 0.78 and specificity 0.90 but only with a prohibitively large uncertainty (48 hours) surrounding the time of transfer. Performance using only standard bedside trend data is no better than chance; improvement may require exploiting additional intra-beat features of monitored waveforms. The full CHEWS appears to capture significant clinical features that are not present in the monitoring data used in this study., by Gregory Alan Ciccarelli., S.M.

Speech is potentially a rich source of biomarkers for detecting and monitoring neuropsychological disorders. Current biomarkers typically comprise acoustic descriptors extracted from behavioral measures of source, filter, prosodic and linguistic cues. In contrast, in this paper, we extract vocal features based on a neurocomputational model of speech production, reflecting latent or internal motor control parameters that may be more sensitive to individual variation under neuropsychological disease. These features, which are constrained by neurophysiology, may be resilient to artifacts and provide an articulatory complement to acoustic features. Our features represent a mapping from a low-dimensional acoustics-based feature space to a high-dimensional space that captures the underlying neural process including articulatory commands and auditory and somatosensory feedback errors. In particular, we demonstrate a neurophysiological vocal source model that generates biomarkers of disease by modeling vocal source control. By using the fundamental frequency contour and a biophysical representation of the vocal source, we infer two neuromuscular time series whose coordination provides vocal features that are applied to depression and Parkinson’s disease as examples. These vocal source coordination features alone, on a single held vowel, outperform or are comparable to other features sets and reflect a significant compression of the feature space., United States. Air Force (Contract No. FA8721-05-C-0002), United States. Air Force (Contract No. FA8702-15- D-0001)

Reliable, real-time heart and respiratory rates are key vital signs used in evaluating the physiological status in many clinical and non-clinical settings. Measuring these vital signs generally requires superficial attachment of physically or logistically obtrusive sensors to subjects that may result in skin irritation or adversely influence subject performance. Given the broad acceptance of ingestible electronics, we developed an approach that enables vital sign monitoring internally from the gastrointestinal tract. Here we report initial proof-of-concept large animal (porcine) experiments and a robust processing algorithm that demonstrates the feasibility of this approach. Implementing vital sign monitoring as a stand-alone technology or in conjunction with other ingestible devices has the capacity to significantly aid telemedicine, optimize performance monitoring of athletes, military service members, and first-responders, as well as provide a facile method for rapid clinical evaluation and triage., United States. Dept. of the Air Force (Air Force Contract FA8721-05-C-0002), United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering, National Institutes of Health (U.S.) (Grant EB000244), National Institutes of Health (U.S.) (Grant T32DK7191-38-S1)