The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Automatic Detection of Voice Impairments by Means of Short-Term Cepstral Parameters and Neural Network Based Detectors
Abstract
It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
Discrete-Time Processing of Speech Signals
Deller, J. R., Hansen, J. H. L., & Proakis, J. G. (1999) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544402 · Cited by 2,458
Clinical measurement of speech and voice
Baken, R. J. (1987) · Cited by 1,465
Pattern Recognition: Statistical, Structural and Neural Approaches
Schalkoff, R. J. (1991) · Medical Entomology and Zoology · Cited by 964
An introduction to computing with neural nets
Lippmann, R. P. (1988) · ACM SIGARCH Computer Architecture News · doi:10.1145/44571.44572 · Cited by 539
A Cepstrum-Based Technique for Determining a Harmonics-to-Noise Ratio in Speech Signals
Krom, G. D. (1993) · Journal of Speech, Language, and Hearing Research · doi:10.1044/jshr.3602.254 · Cited by 320
Normalized noise energy as an acoustic measure to evaluate pathologic voice
Kasuya, H., Ogawa, S., Mashima, K., & Ebihara, S. (1986) · The Journal of the Acoustical Society of America · doi:10.1121/1.394384 · Cited by 256
Cited by
Rusz, J., Cmejla, R., Ruzickova, H., & Ruzicka, E. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3514381 · Cited by 489
Godino-Llorente, J. I., Gomez-Vilda, P., & Blanco-Velasco, M. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.871883 · Cited by 322
Self-citationDetection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach
Fang, S.-H., Tsao, Y., Hsiao, M.-J., Chen, J.-Y., Lai, Y.-H., Lin, F.-C., & Wang, C.-T. (2019) · Journal of Voice · doi:10.1016/j.jvoice.2018.02.003 · Cited by 278
Discrimination of pathological voices using a time-frequency approach
Umapathy, K., Krishnan, S., Parsa, V., & Jamieson, D. G. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.842962 · Cited by 149
Kim, H., Jeon, J., Han, Y. J., Joo, Y., Lee, J., Lee, S., & Im, S. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9113415 · Cited by 97
Muhammad, G., & Alhussein, M. (2021) · IEEE Access · doi:10.1109/access.2021.3090317 · Cited by 70
Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection
Muhammad, G., Mesallam, T. A., Malki, K. H., Farahat, M., Mahmood, A., & Alsulaiman, M. (2012) · Journal of Voice · doi:10.1016/j.jvoice.2012.05.002 · Cited by 66
Fraile, R., Sáenz-Lechón, N., Godino-Llorente, J. I., Osma-Ruiz, V., & Fredouille, C. (2009) · Folia Phoniatrica et Logopaedica · doi:10.1159/000219950 · Cited by 62
Self-citationThe Effect of the MFCC Frame Length in Automatic Voice Pathology Detection
Tirronen, S., Kadiri, S. R., & Alku, P. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2022.03.021 · Cited by 45
Detection of vocal disorders based on phase space parameters and Lyapunov spectrum
Ghasemzadeh, H., Tajik Khass, M., Khalil Arjmandi, M., & Pooyan, M. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.07.002 · Cited by 40
The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review
Barlow, J., Sragi, Z., Rivera‐Rivera, G., Al‐Awady, A., Daşdöğen, Ü., Courey, M. S., & Kirke, D. N. (2024) · Otolaryngology–Head and Neck Surgery · doi:10.1002/ohn.636 · Cited by 25
Akbari, A., & Arjmandi, M. K. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.02.008 · Cited by 22
Low, D. M., Rao, V., Randolph, G., Song, P. C., & Ghosh, S. S. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000516 · Cited by 16
Different performances of machine learning models to classify dysphonic and non-dysphonic voices
Leite, D. R. A., de Moraes, R. M., & Lopes, L. W. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2022.11.001 · Cited by 15
Santana, É. R., Lopes, L., & de Moraes, R. M. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2022.10.001 · Cited by 8
Pakravan, M., & Jahed, M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103410 · Cited by 6
Metadata
- Authors
- Godino-Llorente, J. I., & Gomez-Vilda, P.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-02
- Volume
- 51
- Issue
- 2
- Pages
- 380-384
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2138041026
- Cited by
- 264
- References
- 24
- FWCI
- 6.08
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
