arXiv subjects

Thomas F. Quatieri

Publications and source records attributed to Thomas F. Quatieri.

4 recordsLinked to original sources

arXiv2024

A methodological framework and exemplar protocol for the collection and analysis of repeated speech samples

Speech and language biomarkers have the potential to be regular, objective assessments of symptom severity in several health conditions, both in-clinic and remotely using mobile devices. However, the complex nature of speech and often subtle changes associated with health mean that findings are highly dependent on methodological and cohort choices. These are often not reported adequately in studies investigating speech-based health assessment, hindering the progress of methodological speech research. Our objectives were to) facilitate replicable speech research by presenting an adaptable speech collection and analytical method and design checklist for other researchers to adapt for their own experiments and develop an exemplar protocol that reduces and controls for confounding factors in repeated recordings of speech, including device choice, speech elicitation task and non-pathological variability. The presented protocol comprises the elicitation of read speech, held vowels and a picture description collected with a freestanding condenser microphone, 3 smartphones and a headset. We extracted a set of 14 exemplar speech features. We collected healthy speech from 28 individuals 3 times in 1 day, repeated at the same times 8-11 weeks later, and from 25 individuals on 3 days in 1 week at fixed times. Participant characteristics collected included sex, age, native language status and voice use habits. Before each recording, we collected information on recent voice use, food and drink intake, and emotional state. The extracted features are presented providing a resource of normative values. Speech data collection, processing, analysis and reporting towards clinical research and practice varies widely. Greater harmonisation of study protocols and consistent reporting are urgently required to translate speech processing into clinical research and practice.

cs.SD↗

arXiv2022

Longitudinal Acoustic Speech Tracking Following Pediatric Traumatic Brain Injury

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.

q-bio.NC↗

arXiv2016

Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.

stat.ML↗

arXiv2010

Time-Varying Autoregressions in Speech: Detection Theory and Applications

This article develops a general detection theory for speech analysis based on time-varying autoregressive models, which themselves generalize the classical linear predictive speech analysis framework. This theory leads to a computationally efficient decision-theoretic procedure that may be applied to detect the presence of vocal tract variation in speech waveform data. A corresponding generalized likelihood ratio test is derived and studied both empirically for short data records, using formant-like synthetic examples, and asymptotically, leading to constant false alarm rate hypothesis tests for changes in vocal tract configuration. Two in-depth case studies then serve to illustrate the practical efficacy of this procedure across different time scales of speech dynamics: first, the detection of formant changes on the scale of tens of milliseconds of data, and second, the identification of glottal opening and closing instants on time scales below ten milliseconds.

stat.AP↗