Unveiling early signs of Parkinson's disease via a longitudinal analysis of celebrity speech recordings.

Numerous studies proposed methods to detect Parkinson's disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who publicly disclosed their PD diagnosis provide longitudinal data, allowing the creation of a new corpus, ParkCeleb.

Automating the analysis of eye movement for different neurodegenerative disorders.

The clinical observation and assessment of extra-ocular movements is common practice in assessing neurodegenerative disorders but remains observer-dependent. In the present study, we propose an algorithm that can automatically identify saccades, fixation, smooth pursuit, and blinks using a non-invasive eye tracker. Subsequently, response-to-stimuli-derived interpretable features were elicited that objectively and quantitatively assess patient behaviors.

Interpretable speech features vs. DNN embeddings: What to use in the automatic assessment of Parkinson's disease in multi-lingual scenarios.

Speech-based approaches for assessing Parkinson's Disease (PD) often rely on feature extraction for automatic classification or detection. While many studies prioritize accuracy by using non-interpretable embeddings from Deep Neural Networks, this work aims to explore the predictive capabilities and language robustness of both feature types in a systematic fashion. As interpretable features, prosodic, linguistic, and cognitive descriptors were adopted, while x-vectors, Wav2Vec 2.

Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach.

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity.

Influence of Upper Airway Surgery on Voice and Speech Recognition.

Purpose: Upper airway surgery comprises a set of techniques that modify the anatomy of the vocal tract, including tonsillectomy and septoplasty. The objective of this work is to study the changes in acoustic parameters and the effects on the identification or verification of the speaker through the speech produced after the vocal tract surgeries, comparing them with a control group.

Methods: A prospective study was performed between January 2019 and June 2019 including.

Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease.

Literature documents the impact of Parkinson's Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identification of the disease. This study presents new approaches that are evaluated in three different corpora containing speakers suffering from PD with two main objectives: to investigate the influence of the different phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifiers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the differences in accuracy depending on the manner of articulation.

Effect of Functional Endoscopic Sinus Surgery on Voice and Speech Recognition.

Objective: Functional Endoscopic Sinus Surgery (FESS) is the surgery of choice for nasal polyposis and chronic rhinosinusitis. The aim of our study is to assess the influence of this surgery in the acoustic parameters of voice, and their implications in the systems of identification or verification of the speaker through the speech.

Material And Methods: A prospective study was performed between January 2017 and June 2017 including two groups of patients: those undergoing FESS, and a control group.

Effect of Septoplasty On Voice Performance: Nasalance and Acoustic Study.

Background: Septoplasty is a surgical technique for the correction of the nasal septum that may alter the vocal tract. The aim of our study is to assess whether this technique modifies nasalance and acoustic parameters, and their clinical implications in voice perception.

Methodology: A prospective study was performed between January 2017 and June 2017 including 2 groups of patients: those undergoing septoplasty, and a control group.

Corrigendum: Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

[This corrects the article on p. 1 in vol. 4, PMID: 26835449.

Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

There exist many acoustic parameters employed for pathological assessment tasks, which have served as tools for clinicians to distinguish between normophonic and pathological voices. However, many of these parameters require an appropriate tuning in order to maximize its efficiency. In this work, a group of new and already proposed modulation spectrum (MS) metrics are optimized considering different time and frequency ranges pursuing the maximization of efficiency for the detection of pathological voices.

Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale.

Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable.

An automatic method to detect and track the glottal gap from high speed videoendoscopic images.

Background: The image-based analysis of the vocal folds vibration plays an important role in the diagnosis of voice disorders. The analysis is based not only on the direct observation of the video sequences, but also in an objective characterization of the phonation process by means of features extracted from the recorded images. However, such analysis is based on a previous accurate identification of the glottal gap, which is the most challenging step for a further automatic assessment of the vocal folds vibration.