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.
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Cites
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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
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
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- Godino-Llorente, J. I., & Gomez-Vilda, P. (2004). Automatic Detection of Voice Impairments by Means of Short-Term Cepstral Parameters and Neural Network Based Detectors. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2003.820386
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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