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Pathological Voice Recognition for Vocal Fold Disease
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 47 - Number 13 |
| Year of Publication: 2012 |
| Authors: Pravena D, Dhivya S, Durga Devi A |
10.5120/7250-0314
|
Pravena D, Dhivya S, Durga Devi A . Pathological Voice Recognition for Vocal Fold Disease. International Journal of Computer Applications. 47, 13 ( June 2012), 31-37. DOI=10.5120/7250-0314
Abstract
Pathology is the study and diagnosis of disease. Due to the nature of job, unhealthy habits and voice abuse, the people are subjected to the risk of voice problems. The diagnosis of vocal and voice disorders should be in the early stage otherwise it causes changes in the normal signal. It is well known that most of vocal fold pathologies cause changes in the acoustic voice signal. Therefore, the voice signal can be a useful tool to diagnose them. Acoustic voice analysis can be used to characterize the pathological voices. This paper presents the detection of vocal fold pathology with the aid of the speech signal recorded from the patients. We are going to recognize the disordered voice for vocal fold disease by focusing on the classification of pathological voice from healthy voice based on acoustic features. The method includes two steps. The first step is the extraction of feature vectors based on MFCC. The second is the classification of feature vectors using GMM. The extracted acoustic parameters from the voice signals are used as an input for the MFCC. The main advantage of this method is less computation time and possibility of real-time system development. This report introduces the design and implementation of the proposed system for recognizing pathological and normal voice. Also a description is given about the literature survey done and the implementation of different modules in the system. The result of the proposed system and the scope of improvements are also discussed in the report.
References
- Ce Peng. , Wenxi Chen. , Xin Zhu. ,Daming Wei. , and Baikun Wan. , (2007). Pathological Voice Classification Based on a Single Vowels Acoustic Features. IEEE computer society. Seventh International Conference on Computer and Information Technology. ,.
- lagadish Nayak . , Subhanna Bhat , P. ,(2003). Identification of Voice Disorders using Speech Samples ," IEEE Trans. Speech processing. ,
- Schwarz, R. , Hoppe, U. ,Schuster, M. , Wurzbacher,U. , and Eysholdt. , Lohscheller, J. (2006). Classification of unilateral vocal fold paralysis by endoscopic digital high-speed recordings and inversion of a biomechanical model. IEEE Trans. Biomed. Eng. , vol. 53, no. 6, pp. 10991108.
- Constantine Kotropoulos . , and Gonzalo R. Arce. (2009). Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema. EURASIP Journal on Advances in Signal Processing,
- Parsa, V. , and Jamieson, D. G. (2003). Interactions between speech coders and disordered speech. Speech Communication. ,vol. 40, no. 7, pp. 365385,
- Hawley,M. S. , Green ,P. ,Enderby, P. , Cunningham, S. , and Moore, R. K. , (2005). Speech technology for e-inclusion of people with physical disabilities and disordered speech. INTERSPEECH 05. ,pp. 445448, Lisbon,Portugal
- Oscar Saz. , Javier Simon. ,Ricardo Rodriguez, W. , Eduardo Lleida. , and Carlos Vaquero. , (2009). Analysis of Acoustic Features in Speakers with Cognitive Disorders and Speech Impairments EURASIP Journal on Advances in Signal Processing,
- Ruben Fernandez Pozo. , Jose Luis BlancoMurillo. ,Luis Hernandez Gomez. , Eduardo Lopez Gonzalo. ,JoseAlcazar Ramirez and Doroteo T. Toledano. , (2009). Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques EURASIP Journal on Advances in Signal Processing,
- Alireza Afshordi Dibazar. , Shikanth Narayanan. ,A System for Automatic Detection of Pathological Speech (2002)
- Julian D. Arias-Londono. , Juan I. Godino-Llorente. ,Nicolas Saenz-Lechon. ,Victor Osma-Ruiz. , and German Castellanos-Dominguez. , Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients (2011) IEEE Trans. on biomedical engineering vol. 58, no. 2. ,
- Darcio G. Silva. , Luis C. Oliveira. ,and Mario Andrea. , Jitter Estimation Algorithms for Detection of Pathological Voices(2009) EURASIP Journal on Advances in Signal Processing
- Jianglin Wang. , cheolwoo Jo. ,Vocal Folds Disorder Detection using Pattern Recognition Methods (2007) IEEE EMBS ,23-26(8).
- Karthikeyan Umapathy. , Sridhar Krishnan. ,Donald G. Jamieson. ,Discrimination of Pathological Voices Using a Time-Frequency Approach (2005) IEEE Trans. on biomedical engineering vol. 52, no. 3. ,
- Maria Markaki. , Yannis Stylianou. ,Voice Pathology Detection and Discrimination Based on Modulation Spectral Features (2011) IEEE Trans. on audio, speech, and language processing vol. 19, no. 7. ,
- Fetisova,O. G. , Lamtyugin,D. V. ,Makukha,V. K. ,Voronin,E. M. ,Spectrum analysis of vocalization application for voice pathol-ogy detection (2007) IEEE Trans. The International Conference on Computer as a Tool . ,
- Alireza A. Dibazar. , Theodore W. Berger. ,Shrikanth S. Narayanan. ,Pathological Voice Assessment (2006) IEEE Trans. EMBS Annual International Conference . ,
- Ghazaleh vaziri . , Farshad Almasganj . ,PATHOLOGICAL ASSESSMENT OF VOCAL FOLD NODULES AND POLYP VIA FRACTAL DIMENSION OF PATIENTS VOICES (2008) IEEE Trans. Iran National Science Foundation . ,
- Paulo Rogerio Scalassara. , Maria Eugenia Dajer. , Jamille Lays Marrara. , Carlos Dias Maciel. , Jose Carlos Pereira. ,Analysis of Voice Pathology Evolution Using Entropy Rate (2008) IEEE Trans. International Symposium on Multimedia . ,
- Patricia Henriquez. , Jesus B. Alonso. , Miguel A. Ferrer. , Carlos M. Travieso. , Juan I. Godino-Llorente. , and Fernando Diaz-de-Maria. ,Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics (20009) IEEE Trans. AUDIO, SPEECH, AND LANGUAGE PROCESSING VOL. 17, NO. 6. ,
- Mark Gales. , Steve Young. , The Applications of Hidden Markov Models in Speech Recognition. , Foundations and Trends in Signal Processing, 1,3(2007),195-304.
- Reynolds, D. A. , and Rose,R. C. , Robust text-independent Speaker Identification using Gaussian mixture speaker models, 1(2005) IEEE Trans. On Speech and Audio Processing, vol. 3, pp. 7283,
- Douglas A. Reynolds. , Speaker Identification and verification using Gaussian mixture speaker models, Speech Communi-cation 17 (1995) 91-108, Elsevier.
- G. McLachlan, Mixture Models. New York: Marcel Dekker, 1988.
- A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Royal Stat. vol. 39,pp. 1-38,1977.
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