Multimodal I-vectors to Detect and Evaluate Parkinson's Disease
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Speaker embedding features were previously only studied for speech impairment assessment.
“Speaker embedding features were previously only studied for speech impairment assessment [88]-[90].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [89]
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Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stern, M. B., Dodel, R., et al. (2008) · Movement Disorders · doi:10.1002/mds.22340 · Cited by 7,536 · see citing works
Front-End Factor Analysis for Speaker Verification
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The clinical symptoms of Parkinson's disease.
Sveinbjornsdottir, S. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13691 · Cited by 1,410 · cited 1× · see citing works
“Parkinson’s Disease (PD) is the second most common neurodegenerative disease worldwide after Alzheimer’s, and its incidence and prevalence are rising [1].”
BackgroundAnalysis of i-vector length normalization in speaker recognition systems
Garcia-Romero, D., & Espy-Wilson, C. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-53 · Cited by 962 · see citing works
Motor symptoms in Parkinson’s disease: A unified framework
Moustafa, A. A., Chakravarthy, V. S., Phillips, J. R., Gupta, A., Kéri, S., Polner, B., Frank, M. J., & Jahanshahi, M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.07.010 · Cited by 485 · see citing works
New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease
Orozco‐Arroyave, J. R., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Rátiva, M. C. G., & Nöth, E. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3c643i6hcfzo · Cited by 248 · see citing works
Self-citationGait Disorders in Parkinson's Disease: Assessment and Management
Chen, P., Wang, R., Liou, D.-J., & Shaw, J. (2013) · International journal of gerontology · doi:10.1016/j.ijge.2013.03.005 · Cited by 123 · see citing works
Feature extraction from smartphone inertial signals for human activity segmentation
San-Segundo, R., Montero, J. M., Barra-Chicote, R., Fernández-Martínez, F., & Pardo, J. M. (2015) · Signal Processing · doi:10.1016/j.sigpro.2015.09.029 · Cited by 102 · see citing works
Stamford, J. A., Schmidt, P. N., & Friedl, K. E. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2464354 · Cited by 86 · see citing works
Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review
Oung, Q. W., Muthusamy, H., Lee, H. L., Basah, S. N., Yaacob, S., Sarillee, M., & Lee, C. K. (2015) · Sensors · doi:10.3390/s150921710 · Cited by 70 · see citing works
A smartphone-based system to quantify dexterity in Parkinson's disease patients
Aghanavesi, S., Nyholm, D., Senek, M., Bergquist, F., & Memedi, M. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.05.005 · Cited by 48 · see citing works
Evaluation of the Neurological State of People with Parkinson’s Disease Using i-Vectors
García, N. M. C., Orozco‐Arroyave, J. R., D’Haro, L. F., Dehak, N., & Nöth, E. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-819 · Cited by 24 · see citing works
Self-citationHannink, J., Gaßner, H., Winkler, J., Eskofier, B. M., & Klucken, J. (2017) · Basal Ganglia · doi:10.1016/j.baga.2017.02.002 · Cited by 4 · see citing works
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Geng, 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
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Self-citationAnalysis and Calibration of Lombard Effect and Whisper for Speaker Recognition
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Assessing Alzheimer’s disease from speech using the i-vector approach
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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
Vásquez-Correa, J. C., Bocklet, T., Orozco‐Arroyave, J. R., & Nöth, E. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054348 · Cited by 12 · see citing works
Self-citationSahu, L. P., & Pradhan, G. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-02047-x · Cited by 6 · see citing works
Khaskhoussy, R., & BenAyed, Y. (2022) · International Conference on Machine Vision · doi:10.1117/12.2623240 · Cited by 6 · see citing works
Vásquez-Correa, J. C., Bocklet, T., Orozco‐Arroyave, J. R., & Nöth, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05412 · Cited by 4 · see citing works
Self-citationEnsemble Bag-of-Audio-Words Representation Improves Paralinguistic Classification Accuracy
Gosztolya, G., & Busa‐Fekete, R. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3044465 · Cited by 3 · see citing works
- Authors
- Garcia, N., Vásquez Correa, J. C., Orozco-Arroyave, J. R., & Nöth, E.
- ORCID
- Nöth, Elmar
- Venue
- Interspeech 2018
- Publisher
- ISCA
- Published
- 2018-09-02
- Pages
- 2349-2353
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2889334015
- Cited by
- 13
- References
- 13
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 1
- FWCI
- 9.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- Parkinson's disease, speech analysis, disease detection, disease severity assessment
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