Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.

Introduction: Functional voice disorders are characterized by impaired voice production without primary organic changes, posing challenges for standardized assessment. Current diagnostic methods rely heavily on subjective evaluation, suffering from inter-rater variability. High-speed videoendoscopy (HSV) offers an objective alternative by capturing true intra-cycle vocal fold behavior.

Contrastive Learning Approach for Assessment of Phonological Precision in Patients with Tongue Cancer Using MRI Data.

Magnetic Resonance Imaging (MRI) allows analyzing speech production by capturing high-resolution images of the dynamic processes in the vocal tract. In clinical applications, combining MRI with synchronized speech recordings leads to improved patient outcomes, especially if a phonological-based approach is used for assessment. However, when audio signals are unavailable, the recognition accuracy of sounds is decreased when using only MRI data.

Effects of Instructed Laryngeal Manipulation on Vocal Rise Time.

Objectives: Previous research has shown that instructed manipulation of the false vocal fold activity (FVFA), true vocal fold mass (TVFM), and larynx height (LH) impacted on voice quality. It is not known whether these manipulations have any effect on voice onset. Vocal Rise Time (VRT) is an objective acoustic measure of voice onset, which has potential as an assessment tool in clinical settings.

Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.

Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks.

Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech.

Background: Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined.

Vowel onset measures and their reliability, sensitivity and specificity: A systematic literature review.

Objective: To systematically evaluate the evidence for the reliability, sensitivity and specificity of existing measures of vowel-initial voice onset.

Methods: A literature search was conducted across electronic databases for published studies (MEDLINE, EMBASE, Scopus, Web of Science, CINAHL, PubMed Central, IEEE Xplore) and grey literature (ProQuest for unpublished dissertations) measuring vowel onset. Eligibility criteria included research of any study design type or context focused on measuring human voice onset on an initial vowel.

Machine learning based estimation of hoarseness severity using sustained vowelsa).

Auditory perceptual evaluation is considered the gold standard for assessing voice quality, but its reliability is limited due to inter-rater variability and coarse rating scales. This study investigates a continuous, objective approach to evaluate hoarseness severity combining machine learning (ML) and sustained phonation. For this purpose, 635 acoustic recordings of the sustained vowel /a/ and subjective ratings based on the roughness, breathiness, and hoarseness scale were collected from 595 subjects.

Nyquist Plot Parametrization for Quantitative Analysis of Vibration of the Vocal Folds.

Objectives: The Nyquist plot provides a graphical representation of the glottal cycles as elliptical trajectories in a 2D plane. This study proposes a methodology to parameterize the Nyquist plot with application to support the quantitative analysis of voice disorders.

Methods: We considered high-speed videoendoscopy recordings of 33 functional dysphonia (FD) patients and 33 normophonic controls (NC).

Adult Cochlear Implant Users Versus Typical Hearing Persons: An Automatic Analysis of Acoustic-Prosodic Parameters.

Purpose: The aim of this study was to investigate the speech prosody of postlingually deaf cochlear implant (CI) users compared with control speakers without hearing or speech impairment.

Method: Speech recordings of 74 CI users (37 males and 37 females) and 72 age-balanced control speakers (36 males and 36 females) are considered. All participants are German native speakers and read (The North Wind and the Sun), a standard text in pathological speech analysis and phonetic transcriptions.

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach.

Background: Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject-level and task-related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods.

: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement.

This paper introduces , a mobile application for motor evaluation and monitoring of Parkinson's disease patients. The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls.

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process.