Characterization of Dysphonic Voices by Means of a Filterbank-Based Spectral Analysis: Sustained Vowels and Running Speech

Fraile, Rubén; Godino-Llorente, Juan Ignacio; Sáenz-Lechón, Nicolás; Osma-Ruiz, Víctor; Gutiérrez-Arriola, Juana M.

doi:10.1016/j.jvoice.2012.07.004

Cited by 18 publications

(10 citation statements)

References 36 publications

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“…. This also confirms the findings of Fralie et al [31], who states that "power in bands between 2000 and 6400 Hz is significantly less stable in dysphonic voices".…”

Section: ) Results Of the Proposed Methodsupporting

confidence: 91%

Voice pathology detection using auto-correlation of different filters bank

Al-nasheri

Ali

Muhammad

et al. 2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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This paper investigates the contribution of frequency bands for automatic voice pathology detection. First, the input voice signal is passed through a number of time-domain band-pass filters. The center frequencies are spaced on an octave scale. Each filter output is then divided into overlapping frames. Auto-correlation function is applied to each block to find the first largest peak, in areas other than near the dc value, and its corresponding lag. Therefore, each frame is having only these two features (peak value and lag). As classifier, we use Gaussian mixture models (GMM) and support vector machine (SVM), separately. Two well-known available databases, one in English (MEEI) and the other one in German (SVD), are used in the investigation. The results demonstrate that the most significant frequency range to detect voice pathology is between 1500 Hz and 3500 Hz. Using this filter band and with only two features, the accuracy is above 97% in case of the MEEI database.

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“…. This also confirms the findings of Fralie et al [31], who states that "power in bands between 2000 and 6400 Hz is significantly less stable in dysphonic voices".…”

Section: ) Results Of the Proposed Methodsupporting

confidence: 91%

Voice pathology detection using auto-correlation of different filters bank

Al-nasheri

Ali

Muhammad

et al. 2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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“…As it is seen from that figure, the greatest contribution for detection and classification is achieved in case of filters 4, 5, 6, and 7. This also confirms the findings of Fraile et al [36], which state that "power in bands between 2000 and 6400 Hz is significantly less stable in dysphonic voices." Besides that, as we notice from the experimental result, the highest achieved accuracies occurred in the combined filters in all experiments, because each filter has the ability to detect some components in the specified range of frequencies components than the other filter, and when we combine more than one filter together their frequency range is expanded, which leads to higher accuracy of detection and classification.…”

Section: Discussionsupporting

confidence: 91%

“…For instance, in [37] the authors found that the lower frequencies between 0 ~ 3000 Hz are more suitable for discriminating dysphonic voices than the higher frequencies. In addition, Fraile et al in [36] found that the power in bands between 2000 and 6400 Hz is significantly less stable in dysphonic voices.…”

Section: Introductionmentioning

confidence: 96%

Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions

et al. 2017

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The best achieved accuracies in both detection and classification were varied depending on the band, the correlation function, and the database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. In detection, the highest acquired accuracies when using cross-correlation were 99.809%, 90.979%, and 91.168% in the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and Arabic Voice Pathology Database databases, respectively. However, in classification, the highest acquired accuracies when using cross-correlation were 99.255%, 98.941%, and 95.188% in the three databases, respectively.

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“…A variant of CPP that has been proposed by smoothing the cepstrum and the corresponding parameter is referred to as the smoothed CPP [32]. Studies [33], [34] have additionally used the average spectral energies in low-frequency and high-frequency bands. Features derived from time-frequency decomposition techniques such as adaptive time-frequency transform [35], [36], wavelet transform [37]- [40], modulation spectrum [41]- [43] and empirical mode decomposition [44] have also been investigated for voice pathology detection.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Detection of Pathological Voice Using Glottal Source Features

Kadiri

Alku

2020

IEEE J. Sel. Top. Signal Process.

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Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features.

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Characterization of Dysphonic Voices by Means of a Filterbank-Based Spectral Analysis: Sustained Vowels and Running Speech

Cited by 18 publications

References 36 publications

Voice pathology detection using auto-correlation of different filters bank

Voice pathology detection using auto-correlation of different filters bank

Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions

Analysis and Detection of Pathological Voice Using Glottal Source Features

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