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| 2. |
- Gelzinis, Adas, et al.
(författare)
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Categorizing sequences of laryngeal data for decision support
- 2009
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Ingår i: Electrical and Control Technologies. - Kaunas : IFAC Committee of National Lithuanian Organisation. ; , s. 99-102
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with kernel-based techniques forcategorizing laryngeal disorders based on information extracted fromsequences of laryngeal colour images. The features used tocharacterize a laryngeal image are given by the kernel principalcomponents computed using the $N$-vector of the 3-D colourhistogram. The least squares support vector machine (LS-SVM) isdesigned for categorizing an image sequence into the healthy, nodular and diffuse classes. The kernel functionemployed by the SVM classifier is defined over a pair of matrices, rather than over a pair of vectors. An encouraging classificationperformance was obtained when testing the developed tools on datarecorded during routine laryngeal videostroboscopy.
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| 3. |
- Gelzinis, Adas, et al.
(författare)
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Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders
- 2014
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Ingår i: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). - Piscataway, NJ : IEEE Press. - 9781479945276 - 9781479945269 ; , s. 125-132
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Konferensbidrag (refereegranskat)abstract
- Exploration of various features and different structures of data dependent random forests in screening for laryngeal disorders through analysis of sustained phonation recorded by acoustic and contact microphones is the main objective of this study. To obtain a versatile characterization of voice samples, 14 different sets of features were extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We proposed a new, data dependent random forest-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest was also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the Perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the LP-coefficients and LPCT-coefficients feature sets exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for classification. The proposed data dependent random forest significantly outperformed traditional designs. © 2014 IEEE.
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| 4. |
- Gelzinis, Adas, et al.
(författare)
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Towards video laryngostroboscopy-based automated screening for laryngeal disorders
- 2009
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Ingår i: Proceedings of the 6th International Conference “Models and Analysis of Vocal Emissions for Biomedical Applications”, MAVEBA 2009. - Florence, Italy : Firenze University Press. - 9788864530949 ; , s. 125-128
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with kernel-based techniques for automatedcategorization of laryngeal colour image sequences obtained by videolaryngostroboscopy. Features used to characterize a laryngeal imageare given by the kernel principal components computed using the$N$-vector of the 3-D colour histogram. The least squares supportvector machine (LS-SVM) is designed for categorizing an imagesequence (video) into the healthy, cancerous and noncancerous classes. The kernel function employed by theLS-SVM is defined over a pair of matrices, rather than over a pairof vectors. The classification accuracy of over 85% was obtainedwhen testing the developed tools on data recorded during routinelaryngeal videostroboscopy.
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| 5. |
- Minelga, Jonas, et al.
(författare)
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Comparing Throat and Acoustic Microphones for Laryngeal Pathology Detection from Human Voice
- 2014
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Ingår i: Electrical and Control Technologies. - Kaunas : Kaunas University of Technology. ; , s. 50-53
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Konferensbidrag (refereegranskat)abstract
- The aim of this study was to compare acoustic and throat microphones in the voice pathology detection task. Recordings of sustained phonation /a/ were used in the study. Each recording was characterized by a rather large set of diverse features, 1051 features in total. Classification into two classes, namely normal and pathological, was performed using random forest committees. Models trained using data obtained from the throat microphone provided lower classification accuracy. This is probably due to a narrower frequency range of the throat microphone leading to loss of important information. © Kaunas University of Technology, 2014.
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| 6. |
- Uloza, Virgilijus, et al.
(författare)
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Categorizing Normal and Pathological Voices : Automated and Perceptual Categorization
- 2011
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Ingår i: Journal of Voice. - New York : Mosby-Elsevier. - 0892-1997 .- 1873-4588. ; 25:6, s. 700-708
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The aims of the present study were to evaluate the accuracy of an elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts. Material and Methods: We investigated the effectiveness of 10 different feature sets in the classification of voice recordings of the sustained phonation of the vowel sound /a/ into the healthy and two pathological voice classes, and proposed a new approach to building a sequential committee of support vector machines (SVMs) for the classification. By applying “genetic search” (a search technique used to find solutions to optimization problems), we determined the optimal values of hyper-parameters of the committee and the feature sets that provided the best performance. Four experienced clinical voice specialists who evaluated the same voice recordings served as experts. The “gold standard” for classification was clinically and histologically proven diagnosis. Results: A considerable improvement in the classification accuracy was obtained from the committee when compared with the single feature type-based classifiers. In the experimental investigations that were performed using 444 voice recordings coming from 148 subjects, three recordings from each subject, we obtained the correct classification rate (CCR) of over 92% when classifying into the healthy-pathological voice classes, and over 90% when classifying into three classes (healthy voice and two nodular or diffuse lesion voice classes). The CCR obtained from human experts was about 74% and 60%, respectively. Conclusion: When operating under the same experimental conditions, the automated voice discrimination technique based on sequential committee of SVM was considerably more effective than the human experts.
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| 7. |
- Uloza, Virgilijus, et al.
(författare)
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Combined Use of Standard and Throat Microphones for Measurement of Acoustic Voice Parameters and Voice Categorization
- 2015
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Ingår i: Journal of Voice. - Philadelphia, PA : Elsevier. - 0892-1997 .- 1873-4588. ; 29:5, s. 552-559
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Tidskriftsartikel (refereegranskat)abstract
- Summary: Objective. The aim of the present study was to evaluate the reliability of the measurements of acoustic voice parameters obtained simultaneously using oral and contact (throat) microphones and to investigate utility of combined use of these microphones for voice categorization.Materials and Methods. Voice samples of sustained vowel /a/ obtained from 157 subjects (105 healthy and 52 pathological voices) were recorded in a soundproof booth simultaneously through two microphones: oral AKG Perception 220 microphone (AKG Acoustics, Vienna, Austria) and contact (throat) Triumph PC microphone (Clearer Communications, Inc, Burnaby, Canada) placed on the lamina of thyroid cartilage. Acoustic voice signal data were measured for fundamental frequency, percent of jitter and shimmer, normalized noise energy, signal-to-noise ratio, and harmonic-to-noise ratio using Dr. Speech software (Tiger Electronics, Seattle, WA).Results. The correlations of acoustic voice parameters in vocal performance were statistically significant and strong (r = 0.71–1.0) for the entire functional measurements obtained for the two microphones. When classifying into healthy-pathological voice classes, the oral-shimmer revealed the correct classification rate (CCR) of 75.2% and the throat-jitter revealed CCR of 70.7%. However, combination of both throat and oral microphones allowed identifying a set of three voice parameters: throat-signal-to-noise ratio, oral-shimmer, and oral-normalized noise energy, which provided the CCR of 80.3%.Conclusions. The measurements of acoustic voice parameters using a combination of oral and throat microphones showed to be reliable in clinical settings and demonstrated high CCRs when distinguishing the healthy and pathological voice patient groups. Our study validates the suitability of the throat microphone signal for the task of automatic voice analysis for the purpose of voice screening. Copyright © 2014 The Voice Foundation.
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| 8. |
- Uloza, Virgilijus, et al.
(författare)
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Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening
- 2015
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Ingår i: European Archives of Oto-Rhino-Laryngology. - Heidelberg : Springer Berlin/Heidelberg. - 0937-4477 .- 1434-4726. ; 272:11, s. 3391-3399
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73–1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7 % and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5 %. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60 % and RFC revealed the EER of 7.9 %, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.
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| 9. |
- Uloza, Virgilijus, et al.
(författare)
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Swedish-Lithuanian telemedicine Litmed project in otolaryngology
- 2003
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Ingår i: International Congress Series. - 0531-5131. ; 1240, s. 1407-1410
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Tidskriftsartikel (refereegranskat)abstract
- Telemedicine is becoming a reality as a result of improvements in technology and telecommunications. The task of the otolaryngological part of the pilot international telemedicine Litmed project was devoted to the establishment of telemedicine training and demonstration facilities in cooperation between the Departments of Otolaryngology of the Kaunas University of Medicine (Lithuania) and the Lund University (Sweden). The main areas of action of the Litmed project in otolaryngology were: (1) remote rehabilitation of cochlear implant (CI) patients, and (2) phonosurgery and phoniatrics. The main results of the project were as follows: (a) establishment of telemedicine environment for remote on-line collaboration and planned off-line collaboration by use of recorded video laryngostroboscopic images, voice samples, and graphical and computed tomography (CT) data; (b) possibility to carry through telemedicine sessions for pedagogical and speech training support and for cooperative work of speech therapists from Lund and Kaunas with CI patients; (c) remote consultations and discussion of challenging laryngeal cases; and (d) establishment of a technical environment and practical routines for on-line consultations during laryngeal surgery at the Departments of Otolaryngology of Kaunas and Lund. The Litmed project served to assist medical education and research. Tele-otolaryngology helps to keep constant professional contacts with the specialists from abroad and supports an establishment of a center of reference in tele-otolaryngology in Lithuania.
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| 10. |
- Vaiciukynas, Evaldas, et al.
(författare)
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Exploring similarity-based classification of larynx disorders from human voice
- 2012
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Ingår i: Speech Communication. - Amsterdam : Elsevier. - 0167-6393 .- 1872-7182. ; 54:5, s. 601-610
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Tidskriftsartikel (refereegranskat)abstract
- In this paper identification of laryngeal disorders using cepstral parameters of human voice is researched. Mel-frequency cepstral coefficients (MFCCs), extracted from audio recordings of patient's voice, are further approximated, using various strategies (sampling, averaging, and clustering by Gaussian mixture model). The effectiveness of similarity-based classification techniques in categorizing such pre-processed data into normal voice, nodular, and diffuse vocal fold lesion classes is explored and schemes to combine binary decisions of support vector machines (SVMs) are evaluated. Most practiced RBF kernel was compared to several constructed custom kernels: (i) a sequence kernel, defined over a pair of matrices, rather than over a pair of vectors and calculating the kernelized principal angle (KPA) between subspaces; (ii) a simple supervector kernel using only means of patient's GMM; (iii) two distance kernels, specifically tailored to exploit covariance matrices of GMM and using the approximation of the Kullback-Leibler divergence from the Monte-Carlo sampling (KL-MCS), and the Kullback-Leibler divergence combined with the Earth mover's distance (KL-EMD) as similarity metrics. The sequence kernel and the distance kernels both outperformed the popular RBF kernel, but the difference is statistically significant only in the distance kernels case. When tested on voice recordings, collected from 410 subjects (130 normal voice, 140 diffuse, and 140 nodular vocal fold lesions), the KL-MCS kernel, using GMM with full covariance matrices, and the KL-EMD kernel, using GMM with diagonal covariance matrices, provided the best overall performance. In most cases, SVM reached higher accuracy than least squares SVM, except for common binary classification using distance kernels. The results indicate that features, modeled with GMM, and kernel methods, exploiting this information, is an interesting fusion of generative (probabilistic) and discriminative (hyperplane) models for similarity-based classification. (C) 2011 Elsevier B.V. All rights reserved.
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