Classification of ALS patients based on acoustic analysis of sustained vowel phonations
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
Maxim Vashkevich, Yulia Rushkevich
发表信息：
Biomedical Signal Processing and Control, Volume 65, March 2021, 102350
Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and /i/ to perform automatic classification of ALS patients. A wide range of acoustic features such as MFCC, formants, jitter, shimmer, vibrato, PPE, GNE, HNR, etc. were analysed. We also proposed a new set of acoustic features for characterizing harmonic structure of the vowels. Calculation of these features is based on pitch synchronized voice analysis. A linear discriminant analysis (LDA) was used to classify the phonation produced by patients with ALS and those by healthy individuals. Several algorithms of feature selection were tested to find optimal feature subset for LDA model. The study's experiments show that the most successful LDA model based on 32 features picked out by LASSO feature selection algorithm attains 99.7% accuracy with 99.3% sensitivity and 99.9% specificity. Among the classifiers with a small number of features, we can highlight LDA model with 5 features, which has 89.0% accuracy (87.5% sensitivity and 90.4% specificity).
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of patho…
voice2mode: Phonation Mode Classification in Singing using Self-Supervised Speech Models
University of Birmingham, School of Computer Science, Birmingham, UK（伯明翰大学计算机科学学院）；Carnegie Mellon University, Information Systems, Doha, Qatar（卡内基梅隆大学信息系统）；University of Southern California, Department of Electrical（南加州大学电气工程系）
Assessing Progress of Parkinson s Disease Using Acoustic Analysis of Phonation
Brno University of Technology（布尔诺理工大学）；St. Anne’s University Hospital（圣安妮大学医院）；Central European Institute of Technology（中欧技术研究所）；Masaryk University（马萨里克大学）；Faculty Hospital（大学医院）；Escola Universitaria Politecnica de Mataro（马塔罗大学理工学院）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；Institute for Technological Development and Innovation in Communications (IDeTIC)（通信技术发展与创新研究所（IDeTIC））；University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
University of Augsburg（奥格斯堡大学）；Medical University of Graz（格拉茨医科大学）；audEERING GmbH（audEERING有限公司）；University Hospital of Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University Medical Center Augsburg（奥格斯堡大学医学中心）；Imperial College London（帝国理工学院）
COVID-19 is a global health crisis that has been affecting many aspects of our daily lives throughout the past year. The symptomatology of COVID-19 is heterogeneous with a severity continuum. A considerable proportion of symptoms are related to pathological changes in the vocal system, leading to the assumption that COVID-19 may also affect voice production. For the very first time, the present study aims to investigate voice acoustic correlates of an infection with COVID-19 on the basis of a comprehensive acoustic parameter set. We compare 88 acoustic features extracted from recordings of the…
An Optimal Contact-Mechanically Consistent and Flow-Separation Adapted Modeling of Vocal Fold Dynamics
Department of Mechanical Engineering, Michigan State University（密歇根州立大学机械工程系）；Department of Communicative Sciences and Disorders, Michigan State University（密歇根州立大学沟通科学与障碍系）
Brno University of Technology（布尔诺理工大学）；St. Anne’s University Hospital（圣安妮大学医院）；Masaryk University（马萨里克大学）；Escola Universitaria Politecnica de Mataro（马塔罗理工学院）；University of the Basque Country UPV/EHU（巴斯克大学）
Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classifi…
Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC) and conventional acoustic (dysphonic) features (AF). In…
Vocal Prognostic Digital Biomarkers in Monitoring Chronic Heart Failure: A Longitudinal Observational Study
Centre for Digital Health Interventions, ETH Zurich（苏黎世联邦理工学院数字健康干预中心）；Agentic System Lab, ETH Zurich（苏黎世联邦理工学院代理系统实验室）；Cardiology, University Heart Center Zurich, University Hospital Zurich（苏黎世大学医院心脏中心心脏病学）；Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital（麻省总医院喉外科与嗓音康复中心）；Department of Surgery, Harvard Medical School（哈佛医学院外科学系）；Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School（哈佛医学院医学科学部言语与听觉生物科学与技术）；MGH Institute of Health Professions（麻省总医院健康职业学院）；Centre for Digital Health Interventions, University of St. Gallen（圣加仑大学数字健康干预中心）
In the pathogenesis of COVID-19, impairment of respiratory functions is often one of the key symptoms. Studies show that in these cases, voice production is also adversely affected -- vocal fold oscillations are asynchronous, asymmetrical and more restricted during phonation. This paper proposes a method that analyzes the differential dynamics of the glottal flow waveform (GFW) during voice production to identify features in them that are most significant for the detection of COVID-19 from voice. Since it is hard to measure this directly in COVID-19 patients, we infer it from recorded speech s…
University of Washington（华盛顿大学）；New York University（纽约大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
In this work, we investigate pronunciation differences in English spoken by Singaporean children in relation to their American and British counterparts by conducting Kmeans clustering and Archetypal analysis on selected vowel pairs and approximants. Given that Singapore adopts British English as the institutional standard due to historical reasons, one might expect Singaporean children to follow British pronunciation patterns. Indeed, Singaporean and British children are more similar in their production of syllable-final /r/ -- they do not lower their third formant nearly as much as American c…
Articulatory strategy as a source of variation in acoustic vowel dynamics
Linguistics and English Language, University of Manchester, United Kingdom（曼彻斯特大学语言学与英语语言系，英国）；Linguistics and English Language, Lancaster University, United Kingdom（兰卡斯特大学语言学与英语语言系，英国）
Faculty of Electrical Engineering and Information Technologies, CMUS（斯科普里西里尔和迪乌斯大学电气工程与信息技术学院）；University College London（伦敦大学学院）；TU Dresden（德累斯顿工业大学）
The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of s…
This study addresses unsupervised subword modeling, i.e., learning acoustic feature representations that can distinguish between subword units of a language. We propose a two-stage learning framework that combines self-supervised learning and cross-lingual knowledge transfer. The framework consists of autoregressive predictive coding (APC) as the front-end and a cross-lingual deep neural network (DNN) as the back-end. Experiments on the ABX subword discriminability task conducted with the Libri-light and ZeroSpeech 2017 databases showed that our approach is competitive or superior to state-of-…
Andres Bello University（安德烈斯贝略大学）；Concepcion University（康塞普西翁大学）
This work develops a cross correlation maximization technique, based on statistical concepts, for pattern matching purposes in time series. The technique analytically quantifies the extent of similitude between a known signal within a group of data, by means of a single parameter. Specifically, the method was applied to voice recognition problem, by selecting samples from a given individual recordings of the 5 vowels, in Spanish. The frequency of acquisition of the data was 11.250 Hz. A certain distinctive interval was established from each vowel time series as a representative test function a…
University of Mons（蒙斯大学）；Trinity College Dublin（都柏林圣三一学院）
This paper investigates the temporal excitation patterns of creaky voice. Creaky voice is a voice quality frequently used as a phrase-boundary marker, but also as a means of portraying attitude, affective states and even social status. Consequently, the automatic detection and modelling of creaky voice may have implications for speech technology applications. The acoustic characteristics of creaky voice are, however, rather distinct from modal phonation. Further, several acoustic patterns can bring about the perception of creaky voice, thereby complicating the strategies used for its automatic…
University of Antioquia（安蒂奥基亚大学）；Konecta Group S.A.S.（Konecta集团股份有限公司）
This paper focuses on finding suitable features to robustly recognize emotions and evaluate customer satisfaction from speech in real acoustic scenarios. The classification of emotions is based on standard and well-known corpora and the evaluation of customer satisfaction is based on recordings of real opinions given by customers about the received service during phone calls with call-center agents. The feature sets considered in this study include two speaker models, namely x-vectors and i-vectors, and also the well known feature set introduced in the Interspeech 2010 Paralinguistics Challeng…
Far Eastern Memorial Hospital（远东纪念医院）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Yuan Ze University（元智大学）
Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were c…
The ComParE 2021 COVID-19 Speech Sub-challenge provides a test-bed for the evaluation of automatic detectors of COVID-19 from speech. Such models can be of value by providing test triaging capabilities to health authorities, working alongside traditional testing methods. Herein, we leverage the usage of pre-trained, problem agnostic, speech representations and evaluate their use for this task. We compare the obtained results against a CNN architecture trained from scratch and traditional frequency-domain representations. We also evaluate the usage of Self-Attention Pooling as an utterance-leve…
Automatic speech recognition and voice identification systems are being deployed in a wide array of applications, from providing control mechanisms to devices lacking traditional interfaces, to the automatic transcription of conversations and authentication of users. Many of these applications have significant security and privacy considerations. We develop attacks that force mistranscription and misidentification in state of the art systems, with minimal impact on human comprehension. Processing pipelines for modern systems are comprised of signal preprocessing and feature extraction steps, w…
Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
organization= School of Computer；Communication Sciences, EPFL (\'Ecole polytechnique f\'ed\'erale de Lausanne) , city= Lausanne , country= Switzerland；organization= Eustema S.p.A., Research；Development Centre , city= Naples , country= Italy；organization= UniCamillus-Saint Camillus International University of Health Sciences , city= Rome , country= Italy；organization= Department of Diagnostics；Intervention, Radiation Physics, Biomedical Engineering, Umeå University , city= Umeå , country= Sweden
