Filters








2,499 Hits in 2.4 sec

Automatic speech recognition in neurodegenerative disease

Benjamin G. Schultz, Venkata S. Aditya Tarigoppula, Gustavo Noffs, Sandra Rojas, Anneke van der Walt, David B. Grayden, Adam P. Vogel
2021 International Journal of Speech Technology  
AbstractAutomatic speech recognition (ASR) could potentially improve communication by providing transcriptions of speech in real time.  ...  We tested the performance of three state-of-the-art ASR platforms on two groups of people with neurodegenerative disease and healthy controls.  ...  Automatic speech recognition implementation Automatic speech recognition algorithms were implemented using custom-made Python scripts (Python3.6; Rossum, 2019) that used the SpeechRecognition library (  ... 
doi:10.1007/s10772-021-09836-w fatcat:wibxbdfejzhfviswxvnz2au7ba

Automatic Evaluation of Speech Rhythm Instability and Acceleration in Dysarthrias Associated with Basal Ganglia Dysfunction

Jan Rusz, Jan Hlavnička, Roman Čmejla, Evžen Růžička
2015 Frontiers in Bioengineering and Biotechnology  
Speech rhythm abnormalities are commonly present in patients with different neurodegenerative disorders.  ...  Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences.  ...  Acknowledgments This project was supported by the Czech Science Foundation (GACR 102/12/2230), Czech Ministry of Health (MZ CR 15-28038A), and Charles University in Prague (PRVOUK-P26/LF1/4).  ... 
doi:10.3389/fbioe.2015.00104 pmid:26258122 pmcid:PMC4513571 fatcat:bhqq7xrfgfcqvbkbrjmv3wqenm

Comparison of Speaker Role Recognition and Speaker Enrollment Protocol for conversational Clinical Interviews [article]

Rachid Riad and Hadrien Titeux and Laurie Lemoine and Justine Montillot and Agnes Sliwinski and Jennifer Hamet Bagnou and Xuan Nga Cao and Anne-Catherine Bachoud-Lévi and Emmanuel Dupoux
2020 arXiv   pre-print
We found that our Speaker Role Recognition model gave the best performances. In addition, our study underlined the importance of retraining models with in-domain data.  ...  The automatic analysis of these dialogues could help extract new language markers and speed-up the clinicians' reports.  ...  This work is funded in part by the Agence Nationale pour la  ... 
arXiv:2010.16131v2 fatcat:kfykqtjcijhy3jaa4epewcpzcq

Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial From the Clinical Perspective

Hannah P. Rowe, Sarah E. Gutz, Marc F. Maffei, Katrin Tomanek, Jordan R. Green
2022 Frontiers in Computer Science  
Despite significant advancements in automatic speech recognition (ASR) technology, even the best performing ASR systems are inadequate for speakers with impaired speech.  ...  This diversity is currently poorly characterized and, consequently, difficult to adequately represent in disordered speech ASR corpora.  ...  For individuals with speech impairments, automatic speech recognition (ASR) systems can enhance accessibility and interpersonal communication.  ... 
doi:10.3389/fcomp.2022.770210 fatcat:5xwybjmhxjanblnd2s7g6t24ja

Automated Cross-language Intelligibility Analysis of Parkinson's Disease Patients Using Speech Recognition Technologies

Nina Hosseini-Kivanani, Juan Camilo Vásquez-Correa, Manfred Stede, Elmar Nöth
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop  
We will focus on an intelligibility analysis based on automatic speech recognition systems trained on these three languages.  ...  Speech deficits are common symptoms among Parkinson's Disease (PD) patients.  ...  For example, automatic speech recognition systems are used to evaluate how speech intelligibility is affected by the disease.  ... 
doi:10.18653/v1/p19-2010 dblp:conf/acl/Hosseini-Kivanani19 fatcat:mpgc5tomhvhovnitmnab723huy

Learning diagnostic models using speech and language measures

Bart Peintner, William Jarrold, Dimitra Vergyri, Colleen Richey, Maria Luisa Gorno Tempini, Jennifer Ogar
2008 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
We describe results that show the effectiveness of machine learning in the automatic diagnosis of certain neurodegenerative diseases, several of which alter speech and language production.  ...  We then automatically learned models that predict the diagnosis of the patient using these features.  ...  These qualities, along with the growing evidence that common neurodegenerative diseases alter speech and language production, motivate our research into which automatically produced measures of speech  ... 
doi:10.1109/iembs.2008.4650249 pmid:19163752 fatcat:we3guradxzhddjgowadzw6opqe

Towards the Speech Features of Early-Stage Dementia: Design and Application of the Mandarin Elderly Cognitive Speech Database

Tianqi Wang, Quanlei Yan, Jingshen Pan, Feiqi Zhu, Rongfeng Su, Yi Guo, Lan Wang, Nan Yan
2019 Interspeech 2019  
Details concerning the design of the database, together with our preliminary findings applying automatic speech recognition (ASR), were reported in this study.  ...  Speech and language features have been proven to be useful for the detection of neurodegenerative diseases, such as Alzheimer's disease (AD), and its prodromal stage, mild cognitive impairment (MCI).  ...  The Automatic Speech Recognition (ASR) system has been proposed to identify dementia in early stages [22] .  ... 
doi:10.21437/interspeech.2019-2453 dblp:conf/interspeech/WangYPZSGWY19 fatcat:4ngm6gugd5ejbmxu3sttcbvii4

Biomedical applications of voice and speech processing

Pedro Gómez Vilda
2017 Loquens  
In the group of neurodegenerative diseases of the central nervous system, Alzheimer's disease (AD) or Fronto-Temporal Dementia (FTD) are to be found, whereas in the second group certain pathologies as  ...  All these pathologies produce correlates in speech at different levels: in fluency, in prosody, in articulation or in phonation.  ...  New applications in the field of speech technologies have appeared as advanced signal processing and pattern recognition methods have provided notorious progress in the fields of automatic speech and speaker  ... 
doi:10.3989/loquens.2016.035 fatcat:fg4xoxgvdnbclixu2g33oc77aq

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

Alexandra König, Nicklas Linz, Johannes Tröger, Maria Wolters, Jan Alexandersson, Phillipe Robert
2018 Dementia and Geriatric Cognitive Disorders  
Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items of a semantic category under a time constraint.  ...  All data was an-notated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI and ADRD.  ...  due to neurodegenerative disease.  ... 
doi:10.1159/000487852 pmid:29886493 fatcat:hsxemwklkjephhj7w4hn2jrkr4

A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech

William Jarrold, Adria Rofes, Stephen Wilson, Peter Pressman, Edward Stabler, Marilu Gorno-Tempini
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
However, such human-based analyses of speech are costly and time consuming. Inexpensive off-the-shelf technologies such as speech recognition and part of speech taggers may avoid these problems.  ...  Clinicians often use speech to characterize neurodegenerative disorders. Such characterizations require clinical judgment, which is subjective and can require extensive training.  ...  In total, 70 participants were included: 60 with a neurodegenerative disease and 10 HCs.  ... 
doi:10.1109/embc44109.2020.9176185 pmid:33019300 pmcid:PMC7959106 fatcat:l5l54hcli5gired6e4jqwyb7g4

Neuroanatomy of Shared Conversational Laughter in Neurodegenerative Disease

Peter S. Pressman, Suzanne Shdo, Michaela Simpson, Kuan-Hua Chen, Clinton Mielke, Bruce L. Miller, Katherine P. Rankin, Robert W. Levenson
2018 Frontiers in Neurology  
Here, we investigate the neural correlates of shared laughter in patients with one of a variety of neurodegenerative disease syndromes (N = 75), including Alzheimer's disease (AD), behavioral variant frontotemporal  ...  The probability of each participant with neurodegenerative disease laughing during or shortly after his or her partners' laughter was compared to differences in brain morphology using voxel-based morphometry  ...  Huntington Potter of the Rocky Mountain Alzheimer's Disease Research Center for his support of this publication, and Heidi Chial for edits and comments on this manuscript.  ... 
doi:10.3389/fneur.2018.00464 pmid:29963008 pmcid:PMC6013725 fatcat:ihvnbp3ymvdd5mbgbodr4tcnba

Perceptual Analysis of Speech Signals from People with Parkinson's Disease [chapter]

J. R. Orozco-Arroyave, J. D. Arias-Londoño, J. F. Vargas-Bonilla, Elmar Nöth
2013 Lecture Notes in Computer Science  
Considering the impact of the PD in the intelligibility of the patients, this paper explores the discrimination capability of different perceptual features in the task of automatic classification of speech  ...  The evolution of the disease can get to the point of affecting the intelligibility of the patient's speech.  ...  The authors give a special thanks to all of the patients and collaborators in the foundation "Fundalianza Parkinson-Colombia".  ... 
doi:10.1007/978-3-642-38637-4_21 fatcat:tccaalxpg5e6lelaxsuffez7c4

Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech [article]

Ying Qin, Wei Liu, Zhiyuan Peng, Si-Ioi Ng, Jingyu Li, Haibo Hu, Tan Lee
2021 arXiv   pre-print
Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models.  ...  Alzheimer's disease (AD) is a progressive neurodegenerative disease and recently attracts extensive attention worldwide.  ...  This paper has presented a novel and light-weight framework to perform AD recognition through spontaneous speech, in which a pre-trained ASR model is tailored and adapted to the intended task.  ... 
arXiv:2110.01493v1 fatcat:epkxhdupifdehfwzvky6bli2k4

Pathological Voice Signal Analysis Using Machine Learning Based Approaches

Yahia Alemami, Laiali Almazaydeh
2017 Computer and Information Science  
In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease  ...  PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control.  ...  In this work, we develop an efficient algorithm for automatic classification of voice signal features to detect abnormalities in speech in order to predict PD.  ... 
doi:10.5539/cis.v11n1p8 fatcat:oxe5cp4oafb2fdni4mjtf6fqya

Vocal Markers from Sustained Phonation in Huntington's Disease

Rachid Riad, Hadrien Titeux, Laurie Lemoine, Justine Montillot, Jennifer Hamet Bagnou, Xuan-Nga Cao, Emmanuel Dupoux, Anne-Catherine Bachoud-Lévi
2020 Interspeech 2020  
Disease-modifying treatments are currently assessed in neurodegenerative diseases.  ...  Huntington's Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers.  ...  Acknowledgements We are very thankful to the patients that participated in our study. We thank Agnes Sliwinski, Katia Youssov, Laurent  ... 
doi:10.21437/interspeech.2020-1057 dblp:conf/interspeech/RiadTLMBCDB20 fatcat:tg2cg5o3dbculcd3pxazj77fca
« Previous Showing results 1 — 15 out of 2,499 results