Phonetic-class based correlation analysis for severity of dysphonia

Tulics, Miklos Gabriel; Vicsi, Klára

doi:10.1109/coginfocom.2017.8268210

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…Based on our previous works (Kazinczi et al 2015;Tulics and Vicsi 2017), a total of 33 acoustic parameters were selected and measured per patient voice sample, including jitter, shimmer, HNR and the first component (c1) of the mel-frequency cepstral coefficients measured on vowel [E], and Soft Phonation Index (SPI) and Empirical mode decomposition (EMD) based frequency band ratios on different phonetic classes. We can ask the question whether these acoustic parameters are really suitable for modelling the assessments of the specialists.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Our previous research has confirmed that acoustic parameters like jitter, shimmer, HNR and the first component (c1) of the mel-frequency cepstral coefficients (referred to as 'mfcc01') are useful in the automatic classification of healthy and pathological voices using continuous speech (Vicsi et al 2011;Kazinczi et al 2015;Grygiel et al 2012). Moreover, in Tulics and Vicsi (2017) we demonstrated that these parameters correlate with the severity of dysphonia, as well as Soft Phonation Index (SPI) and Empirical mode decomposition (EMD) based frequency band ratios acoustic parameters measured on different phonetic classes (for example nasals, vowels, fricatives, etc.). In this research jitter, shimmer, HNR mfcc01 and frequency band ratios were used as input features.…”

Section: Introductionmentioning

confidence: 91%

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The automatic assessment of the severity of dysphonia

Tulics

Vicsi

2019

Int J Speech Technol

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Perceptual evaluation of the patient's voice is the most commonly used method in everyday clinical practice. We propose an automatic approach for the prediction of severity of some types of organic and functional dysphonia. By means of an unsupervised learning method, we have demonstrated that acoustic parameters measured on different phonetic classes are suitable for modelling the four grade assessments of the specialists (RBH subjective scale from 0 to 3). In this study, the overall hoarseness H was examined. Four specialists were asked to determine the severity of dysphonia. A k-means cluster analysis was performed for the decision of each specialist separately; the average accuracy of the four-grade classification was 0.46. The four-grade classification has been surprisingly close to the subjective judgements. Moreover, automatic estimation of severity of dysphonia was also determined. Linear regression and RBF kernel regression models were compared. The average rating of the four specialists were used as target in the experiments. Low RMSE and high correlation measures were obtained between the automatically predicted severity and perceptual assessments. The best RMS value of H was 0.45 for the model with RBF kernel, however, a simpler linear model provided the highest correlation value of 0.85, using only eight acoustic parameters.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

The automatic assessment of the severity of dysphonia

Tulics

Vicsi

2019

Int J Speech Technol

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“…CogInfoCom covers several disciplines appearing in applications and research areas also. CogInfoCom is available technology from socio-cognitive ICT [55][56][57][58][59][60][61][62][63][64][65] to cognitive aided engineering [66][67][68][69][70][71][72][73][74][75][76][77] and its related aspects in terms of online collaborative systems and virtual reality solutions [78][79][80], teaching-learning [81][82][83][84][85][86] and human cognitive interfaces such as braincomputer interfaces (BCIs) [87][88][89][90][91][92] and medicals [93][94][95][96].…”

Section: Human-computer Interfaces and Cognitive Infocommunicationmentioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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Brain research is one of the most significant research areas of the last decades, in which many developments and modern engineering technologies are applied. The electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering research fields. Objective: The main goal of this research was to develop a Brain-Computer Interface (BCI) system to observe the level of vigilance calculated by Think Gear-ASIC Module (TGAM1) technology and to evaluate the output with learning efficiency tests applied in cognitive neuroscience. Methods: The performance of the BCI system is evaluated in a comparative study. The BCI system was tested by thirty-two test subjects and the attention level output was compared by the Psychology Experiment Building Language's (PEBL's) Corsi block test (P CORSI) and PEBL's Ebbinghaus procedure (P EBBINGHAUS) tasks. Results: Using the BCI, we have shown statistically significant results between the BCI and the conventional cognitive neuroscience tests. The correlation between the tests and the average attention of the BCI was slightly strong for P CORSI Total Score (r=.63, p<.01 (2-tailed) and slightly strong for P EBBINGHAUS Total Correct (r=-.71, p<.01 (2-tailed). The average level of attention measured by the BCI system during the P CORSI test was 49.00%, while in case of the P EBBINGHAUS test it was 52.41% on all samples. Conclusion: The developed BCI system has a significant correlation with P CORSI and P EBBINGHAUS cognitive neuroscience tests. The BCI system is capable of observing attentional vigilance continuously. Significance: The developed BCI system is applicable to observe vigilance level in realtime while the level of attention depends on activities.

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