Name
Abstract | ||
|---|---|---|
Support Vector Machines (SVMs) have become a popular tool for discriminative classification. An exciting area of recent application of SVMs is in speech processing. In this paper discriminatively trained SVMs have been introduced as a novel approach for the automatic detection of voice impairments. SVMs have a distinctly different modelling strategy in the detection of voice impairments problem, compared to other methods found in the literature (such a Gaussian Mixture or Hidden Markov Models): the SVM models the boundary between the classes instead of modelling the probability density of each class. In this paper it is shown that the scheme proposed fed with short-term cepstral and noise parameters can be applied for the detection of voice impairments with a good performance. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11613107_19 | NOLISP |
Keywords | Field | DocType |
voice disorder,hidden markov models,support vector machine,support vector machines,automatic detection,paper discriminatively,different modelling strategy,voice impairments problem,voice impairment,discriminative classification,gaussian mixture,svm model,probability density,speech processing,hidden markov model | Speech processing,Pattern recognition,Markov model,Computer science,Support vector machine,Cepstrum,Speech recognition,Gaussian,Artificial intelligence,Hidden Markov model,Discriminative model,Mixture model | Conference |
Volume | ISSN | ISBN |
3817 | 0302-9743 | 3-540-31257-9 |
Citations | PageRank | References |
7 | 0.55 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Ignacio Godino-Llorente | 1 | 182 | 30.35 |
| Pedro Gómez Vilda | 2 | 289 | 52.48 |
| Nicolás Sáenz-Lechón | 3 | 173 | 17.21 |
| Manuel Blanco-Velasco | 4 | 314 | 30.57 |
| Fernando Cruz-Roldán | 5 | 111 | 19.33 |
| Miguel Angel Ferrer-Ballester | 6 | 7 | 0.55 |