Name
Title | ||
|---|---|---|
An improved method for voice pathology detection by means of a HMM-based feature space transformation |
Abstract | ||
|---|---|---|
This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.03.019 | Pattern Recognition |
Keywords | Field | DocType |
hmm-based feature space transformation,original input space,statistical transformation,automatic detection,proposed feature space transformation,classification error,pathological voice,improved method,new feature,voice pathology detection,feature transformation technique,classification stage,original feature vector,knn,neural network,fft,gmm,feature vector,mel frequency cepstral coefficient,fourier transform,feature space,pca,hmm,roc curve,snr,em,mixture model,nne,svm,hidden markov models,mfcc,lpc,discriminant analysis,markov model,se,hidden markov model | Speech processing,Mel-frequency cepstrum,Computer science,Speaker recognition,Artificial intelligence,Audio signal processing,Feature vector,Pattern recognition,Feature (computer vision),Support vector machine,Speech recognition,Hidden Markov model,Machine learning | Journal |
Volume | Issue | ISSN |
43 | 9 | Pattern Recognition |
Citations | PageRank | References |
20 | 0.81 | 30 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julián D. Arias-Londoño | 1 | 172 | 17.48 |
| Juan I. Godino-Llorente | 2 | 177 | 12.38 |
| Nicolás Sáenz-Lechón | 3 | 173 | 17.21 |
| Víctor Osma-Ruiz | 4 | 164 | 17.26 |
| German Castellanos-Dominguez | 5 | 258 | 51.21 |