Automatic evaluation of parkinson's speech — acoustic, prosodic and voice related cues
Abstract
Articulation and phonation is affected in 70 % to 90 % of patients with Parkinson’s disease (PD). This study focuses on the question whether speech carries information about 1. PD being present at a speaker or not, and 2. estimating the sever-ity of PD (if present). We first perform classification experi-ments focusing on the automatic detection of PD as a 2-class problem (PD vs. healthy speakers). The detection of severity is described as a 3-class task based on the Unified Parkinson’s Disease Rating Scale (UPDRS) ratings. We employ acous-tic, prosodic and glottal features on different kinds of speech tests: various syllable repetition tasks, read sentences and texts, and monologues. Classification is performed in either case by SVMs. We report recognition results of 81.9 % when trying to differentiate between normally speaking persons and speakers with PD. With system fusion we achieved a recognition results of 59.1 % on the task of UPDRS classification. Index Terms: Parkinson’s Disease, pathologic speech, speech analysis
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Conventional speech intelligibility assessment approaches often require the contents spoken by different speakers to be the same.
“This flexible design is in contrast to conventional speech intelligibility assessment approaches that often require the contents spoken by different speakers to be the same [96], [97], [90].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [97]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Acoustic Theory of Speech Production
Fant, G. (1971) · De Gruyter eBooks · doi:10.1515/9783110873429 · Cited by 2,966 · see citing works
Opensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642 · see citing works
Classification by pairwise coupling
Hastie, T., & Tibshirani, R. J. (1998) · The Annals of Statistics · doi:10.1214/aos/1028144844 · Cited by 1,304 · see citing works
Synthesis of Voiced Sounds From a Two-Mass Model of the Vocal Cords
Ishizaka, K., & Flanagan, J. L. (1972) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1972.tb02651.x · Cited by 899 · see citing works
Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech TestsPreprint
Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2009) · Nature Precedings · doi:10.1109/tbme.2009.2036000 · Cited by 833 · see citing works
Logemann, J. A., Fisher, H. B., Boshes, B., & Blonsky, E. R. (1978) · Journal of Speech and Hearing Disorders · doi:10.1044/jshd.4301.47 · Cited by 695 · see citing works
Rusz, J., Čmejla, R., Ruzickova, H., & Růžička, E. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3514381 · Cited by 498 · see citing works
Advances in the treatment of Parkinson's disease
Singh, N., Pillay, V., & Choonara, Y. E. (2007) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2006.11.009 · Cited by 396 · see citing works
Acoustic analysis of Parkinsonian speech I: Speech characteristics and L-Dopa therapy
Goberman, A. M., & COELHO, C. A. (2002) · Neurorehabilitation · doi:10.3233/nre-2002-17310 · Cited by 120 · see citing works
Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis
Bocklet, T., Nöth, E., Stemmer, G., Růžičková, H., & Rusz, J. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163978 · Cited by 82 · see citing works
Self-citationA characterization of the prosodic loss in Parkinson's disease
Darkins, A. W., Fromkin, V. A., & Benson, D. F. (1988) · Brain and Language · doi:10.1016/0093-934x(88)90142-3 · Cited by 82 · see citing works
The Intonation–Syntax Interface in the Speech of Individuals With Parkinson’s Disease
MacPherson, M. K., Huber, J. E., & Snow, D. P. (2010) · Journal of Speech Language and Hearing Research · doi:10.1044/1092-4388(2010/09-0079 · Cited by 31 · see citing works
Age and gender recognition based on multiple systems - early vs. late fusion
Bocklet, T., Stemmer, G., Zeißler, V., & Nöth, E. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-748 · Cited by 31 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Automatic detection of Parkinson's disease in running speech spoken in three different languages
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Daqrouq, K. O., Skodda, S., Rusz, J., & Nöth, E. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4939739 · Cited by 218 · see citing works
Self-citationMoro-Velázquez, L., Gómez-García, J. A., Arias-Londoño, J. D., Dehak, N., & Godino-Llorente, J. I. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102418 · Cited by 185 · see citing works
The detection of Parkinson disease using the genetic algorithm and SVM classifier
Soumaya, Z., Drissi, T. B., Nsiri, B., Korkmaz, Y., & Ammoumou, A. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107528 · Cited by 173 · see citing works
Orozco‐Arroyave, J. R., Belalcázar-Bolaños, E. A., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Skodda, S., Rusz, J., Daqrouq, K. O., Hönig, F., & Nöth, E. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2467375 · Cited by 132 · see citing works
Self-citationTowards an automatic evaluation of the dysarthria level of patients with Parkinson's disease
Vásquez-Correa, J. C., Orozco‐Arroyave, J. R., Bocklet, T., & Nöth, E. (2018) · Journal of Communication Disorders · doi:10.1016/j.jcomdis.2018.08.002 · Cited by 130 · see citing works
Self-citationAnalysis of acoustic space variability in speech affected by depression
Cummins, N., Sethu, V., Epps, J., Schnieder, S., & Krajewski, J. (2015) · Speech Communication · doi:10.1016/j.specom.2015.09.003 · Cited by 120 · see citing works
Moro-Velázquez, L., Gómez-García, J. A., Godino-Llorente, J. I., Villalba, J., Orozco‐Arroyave, J. R., & Dehak, N. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.11.001 · Cited by 92 · see citing works
X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech
Jeancolas, L., Petrovska‐Delacrétaz, D., Mangone, G., Benkelfat, B.-E., Corvol, J., Vidailhet, M., Lehéricy, S., & Benali, H. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.578369 · Cited by 75 · see citing works
Godino-Llorente, J. I., Shattuck‐Hufnagel, S., Choi, J.-Y., Moro-Velázquez, L., & Gómez-García, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189583 · Cited by 73 · see citing works
Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., & Nöth, E. (2015) · Expert Systems · doi:10.1111/exsy.12106 · Cited by 60 · see citing works
Self-citationGeng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48 · see citing works
Jeancolas, L., Benali, H., Benkelfat, B.-E., Mangone, G., Corvol, J., Vidailhet, M., Lehéricy, S., & Petrovska‐Delacrétaz, D. (2017) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip.2017.8075567 · Cited by 46 · see citing works
Vásquez-Correa, J. C., Arias‐Vergara, T., Schuster, M., Orozco‐Arroyave, J. R., & Nöth, E. (2020) · Speech Communication · doi:10.1016/j.specom.2020.07.005 · Cited by 44 · see citing works
Self-citationSubspace-Based Learning for Automatic Dysarthric Speech Detection
Janbakhshi, P., Kodrasi, I., & Bourlard, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3044503 · Cited by 32 · see citing works
Automatic detection of parkinson's disease from words uttered in three different languages
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Skodda, S., Rusz, J., & Nöth, E. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-375 · Cited by 30 · see citing works
Self-citationUnobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices
Arias‐Vergara, T., Vásquez-Correa, J. C., Orozco‐Arroyave, J. R., Klumpp, P., & Nöth, E. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462332 · Cited by 30 · see citing works
Self-citationRaj, K. D., Lal, G. J., Gopalakrishnan, E. A., Sowmya, V., & Orozco‐Arroyave, J. R. (2024) · IEEE Access · doi:10.1109/access.2024.3416444 · Cited by 29 · see citing works
Surfboard: Audio Feature Extraction for Modern Machine Learning
Lenain, R., Weston, J., Shivkumar, A., & Fristed, E. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2879 · Cited by 28 · see citing works
Automatic speech recognition: A primer for speech-language pathology researchers
Keshet, J. (2018) · International Journal of Speech-Language Pathology · doi:10.1080/17549507.2018.1510033 · Cited by 27 · see citing works
Wang, Q., Fu, Y. H. H., Shao, B., Chang, L., Ren, K., Chen, Z., & Ling, Y. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1036588 · Cited by 25 · see citing works
Comparison of Acoustic Voice Features Derived From Mobile Devices and Studio Microphone Recordings
Fahed, V. S., Doheny, E. P., Busse, M. E., Hoblyn, J. C., & Lowery, M. M. (2022) · Journal of Voice · doi:10.1016/j.jvoice.2022.10.006 · Cited by 25 · see citing works
Kodali, M., Kadiri, S. R., & Alku, P. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2023.101548 · Cited by 24 · see citing works
Pompili, A., Abad, A., Romano, P., Martins, I. P., Cardoso, R., Santos, H., Carvalho, J., Guimarães, I., & Ferreira, J. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64206-2_46 · Cited by 20 · see citing works
Moudden, I. E., Ouzir, M., & Elbernoussi, S. (2017) · Technology and Health Care · doi:10.3233/thc-170824 · Cited by 19 · see citing works
Arasteh, S. T., Ríos-Urrego, C. D., Nöth, E., Maier, A., Yang, S. H., Rusz, J., & Orozco‐Arroyave, J. R. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2108 · Cited by 22 · see citing works
Self-citationJeancolas, L., Mangone, G., Corvol, J., Vidailhet, M., Lehéricy, S., Benkelfat, B.-E., Benali, H., & Petrovska‐Delacrétaz, D. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2825 · Cited by 18 · see citing works
Malekroodi, H. S., Lee, B.-I., & Yi, M. (2025) · Bioengineering · doi:10.3390/bioengineering12111279 · Cited by 17 · see citing works
Blood Glucose Estimation From Voice: First Review of Successes and Challenges
Sidorova, J., Carbonell, P., & Čukić, M. (2020) · Journal of Voice · doi:10.1016/j.jvoice.2020.08.034 · Cited by 17 · see citing works
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Geng, M., Liu, S., Yu, J., Xie, X., Hu, S., Ye, Z., Jin, Z., Liu, X., & Meng, H. M. L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-60 · Cited by 16 · see citing works
Kodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2253 · Cited by 16 · see citing works
Ríos-Urrego, C. D., Vásquez-Correa, J. C., Orozco‐Arroyave, J. R., & Nöth, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58323-1_36 · Cited by 14 · see citing works
Self-citationEffect of acoustic conditions on algorithms to detect Parkinson's disease from speech
Vásquez-Correa, J. C., Serrà, J., Orozco‐Arroyave, J. R., Vargas‐Bonilla, J. F., & Nöth, E. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953121 · Cited by 14 · see citing works
Self-citationde Oliveira, G. C., Pah, N. D., Ngo, Q. C., Yoshida, A., Gomes, N. B., Papa, J. P., & Kumar, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109565 · Cited by 12 · see citing works
On the harmonic-to-noise ratio as an acoustic cue of vocal timbre of Parkinson speakers
Kacha, A., Mertens, C., Grenez, F., Skodda, S., & Schoentgen, J. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.09.004 · Cited by 12 · see citing works
Self-citationKodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414283 · Cited by 12 · see citing works
Ríos-Urrego, C. D., Vásquez-Correa, J. C., Orozco‐Arroyave, J. R., & Nöth, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83527-9_37 · Cited by 11 · see citing works
Self-citationClapham, R. P., Martens, J., van Son, R. J. J. H., Hilgers, F. J. M., van den Brekel, M. W. M., & Middag, C. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.10.001 · Cited by 10 · see citing works
Moro-Velázquez, L., & Dehak, N. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-65654-6_3 · Cited by 9 · see citing works
Automatic Detection of Parkinson’s Disease from Compressed Speech Recordings
Orozco‐Arroyave, J. R., García, N., Vargas‐Bonilla, J. F., & Nöth, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24033-6_10 · Cited by 9 · see citing works
Self-citationPurohit, T., Ruvolo, B., Orozco‐Arroyave, J. R., & Magimai.-Doss, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890852 · Cited by 8 · see citing works
Bilingual Dual-Head Deep Model for Parkinson’s Disease Detection from Speech
Quatra, M. L., Orozco‐Arroyave, J. R., & Siniscalchi, M. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889445 · Cited by 6 · see citing works
Kadiri, S. R., Kodali, M., & Alku, P. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2531 · Cited by 6 · see citing works
Sidorova, J., Karlsson, S., Rosander, O., Berthier, M. L., & Moreno‐Torres, I. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2908365 · Cited by 6 · see citing works
Automatic Detection of Parkinson's Disease from Words Uttered in Three Different Languages
Arabia, S. (2014) · Cited by 5 · see citing works
Applying X-Vectors on Pathological Speech After Larynx Removal
Scheuerer, R., Haderlein, T., Nöth, E., & Bocklet, T. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688278 · Cited by 4 · see citing works
Self-citationCampi, M., Peters, G. W., & Toczydłowska, D. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284667 · Cited by 4 · see citing works
Phonological i-Vectors to Detect Parkinson’s Disease
García-Ospina, N., Arias‐Vergara, T., Vásquez-Correa, J. C., Orozco‐Arroyave, J. R., Cerňak, M., & Nöth, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00794-2_50 · Cited by 4 · see citing works
Self-citationA Review on Automatic Assessment and Detection of Pathological Speech
Batra, A., & Das, P. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4713-3_40 · Cited by 3 · see citing works
Kumari, R., & Ramachandran, P. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106642 · Cited by 3 · see citing works
Yu-hong, M., Zheng, F., Lu, J., & Wang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-32543-4 · Cited by 2 · see citing works
Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator
Hammami, İ. (2019) · International Journal of Applied Mathematics Electronics and Computers · doi:10.18100/ijamec.458230 · Cited by 2 · see citing works
Language Generalization Using Active Learning in the Context of Parkinson’s Disease Classification
Moreno-Acevedo, S. A., Ríos-Urrego, C. D., Vásquez-Correa, J. C., Rusz, J., Nöth, E., & Orozco‐Arroyave, J. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_31 · Cited by 2 · see citing works
Self-citationKünstliche Intelligenz für die Analyse pathologischer Sprache
Bocklet, T., Nöth, E., & Riedhammer, K. (2023) · Sprache · Stimme · Gehör · doi:10.1055/a-2089-5778 · Cited by 1 · see citing works
Self-citationKodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07542 · Cited by 1 · see citing works
A Review on Parkinson Disease Classifier Using Patient Voice Features
Holkar, P. G. A. P. (2015) · International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering · doi:10.15662/ijareeie.2015.0405005
Automated analysis of non-verbal behaviour of schizophrenia patients
Chakraborty, D. (2019) · doi:10.32657/10220/48025
Jessiman, L. J., Murray, G., & Braley, M. (2018)
Campi, M., Peters, G. W., & Toczydłowska, D. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4173535
Information Fusion for Seveirty Detection using Machine Learning in Data Mining Healthcare
Gupta, V., & Bhambu, P. (2023) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece57866.2023.10151332
Voice and Speech in Parkinson’s Disease: Advancements in Early Diagnosis and Treatment
Varsha, K., & Verma, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3942-7_1
Mikhnevich, A. V., & Vashkevich, M. I. (2025) · Doklady BGUIR · doi:10.35596/1729-7648-2025-23-6-96-102
Wagner, D., Baumann, I., Engert, N., Nöth, E., Riedhammer, K., & Bocklet, T. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434680
Self-citationAdversarial attack detection in voice-based Parkinson’s disease diagnosis using machine learning
Quamar, D., & Kumar, V. D. A. (2026) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2026.2711097
- Authors
- Bocklet, T., Steidl, S., Nöth, E., & Skodda, S.
- Venue
- Interspeech 2013
- Publisher
- ISCA
- Published
- 2013-08-25
- Pages
- 1149-1153
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2293992264
- Cited by
- 66
- References
- 14
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 2 · 2023: 8 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 5 · 2018: 6 · 2017: 6 · 2016: 2 · 2015: 6 · 2014: 2
- FWCI
- 25.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Parkinson's disease, speech analysis, automatic disease detection, disease severity assessment, Unified Parkinson's Disease Rating Scale, acoustic features, prosodic features, glottal features, speech classification, support vector machine, system fusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
