Objective automatic assessment of rehabilitative speech treatment in Parkinson's disease

Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.Comment: 43 pages, 5 figures, 6 table

Analysis of the influence of signal compression techniques for voice disorder detection through filter-banked based features

En este artículo se comparan los resultados de utilizar señales de voz comprimidas frente a señales de voz sin comprimir para detectar de forma automática anomalías vocales. Las técnicas de codificación y compresión de voz usadas en este estudio son las mismas que se utilizan de forma estándar en los sistemas de telefonía fija, móvil e IP, y las técnicas de caracterización y clasificación usadas también están dentro de las más utilizadas para la detección automática de anomalías de voz. Los resultados obtenidos permiten concluir que es posible utilizar señales de voz comprimidas para detección automática de patologías vocales sin detrimento en el porcentaje de acierto en el diagnóstico, lo que haría posible la implementación de sistemas de telediagnóstico automático de patologías vocales.This paper compares the results of using compressed voice signals versus uncompressed speech signals to automatically detect voice abnormalities. Coding techniques and voice compression used in this study are the same as those used by default in the fixed, mobile and ip telephony systems, and techniques of characterization and classification used are also among the most used for detecting automatic speech abnormalities. The results obtained indicate that it is possible to use compressed voice signals for automatic detection of vocal pathologies without compromising the success rate in the diagnosis, which would make the implementation of automatic remote diagnosis of vocal pathologies possible

Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech:Insights from the Parkinson’s Voice Initiative

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies