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Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals

Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G

J Med Syst, 2016 Jan;40(1):20.
PMID: 26531753 DOI: 10.1007/s10916-015-0392-2

Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed.

Matched MeSH terms: Voice Disorders/physiopathology*
Reliability of OperaVOX against Multidimensional Voice Program (MDVP)

Mat Baki M, Wood G, Alston M, Ratcliffe P, Sandhu G, Rubin JS, et al.

Clin Otolaryngol, 2015 Feb;40(1):22-8.
PMID: 25263076 DOI: 10.1111/coa.12313

OBJECTIVE: To evaluate the agreement between OperaVOX and MDVP.
DESIGN: Cross sectional reliability study.
SETTING: University teaching hospital.
METHODS: Fifty healthy volunteers and 50 voice disorder patients had supervised recordings in a quiet room using OperaVOX by the iPod's internal microphone with sampling rate of 45 kHz. A five-seconds recording of vowel/a/was used to measure fundamental frequency (F0), jitter, shimmer and noise-to-harmonic ratio (NHR). All healthy volunteers and 21 patients had a second recording. The recorded voices were also analysed using the MDVP. The inter- and intrasoftware reliability was analysed using intraclass correlation (ICC) test and Bland-Altman (BA) method. Mann-Whitney test was used to compare the acoustic parameters between healthy volunteers and patients.
RESULTS: Nine of 50 patients had severe aperiodic voice. The ICC was high with a confidence interval of >0.75 for the inter- and intrasoftware reliability except for the NHR. For the intersoftware BA analysis, excluding the severe aperiodic voice data sets, the bias (95% LOA) of F0, jitter, shimmer and NHR was 0.81 (11.32, -9.71); -0.13 (1.26, -1.52); -0.52 (1.68, -2.72); and 0.08 (0.27, -0.10). For the intrasoftware reliability, it was -1.48 (18.43, -21.39); 0.05 (1.31, -1.21); -0.01 (2.87, -2.89); and 0.005 (0.20, -0.18), respectively. Normative data from the healthy volunteers were obtained. There was a significant difference in all acoustic parameters between volunteers and patients measured by the Opera-VOX (P

Matched MeSH terms: Voice Disorders/physiopathology*
Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?

Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-Nasheri A, Mesallam TA, et al.

J Voice, 2017 May;31(3):386.e1-386.e8.
PMID: 27745756 DOI: 10.1016/j.jvoice.2016.09.009

A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.

Matched MeSH terms: Voice Disorders/physiopathology
Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model

Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G

J Voice, 2016 Nov;30(6):757.e7-757.e19.
PMID: 26522263 DOI: 10.1016/j.jvoice.2015.08.010

BACKGROUND AND OBJECTIVE: Automatic voice pathology detection using sustained vowels has been widely explored. Because of the stationary nature of the speech waveform, pathology detection with a sustained vowel is a comparatively easier task than that using a running speech. Some disorder detection systems with running speech have also been developed, although most of them are based on a voice activity detection (VAD), that is, itself a challenging task. Pathology detection with running speech needs more investigation, and systems with good accuracy (ACC) are required. Furthermore, pathology classification systems with running speech have not received any attention from the research community. In this article, automatic pathology detection and classification systems are developed using text-dependent running speech without adding a VAD module.
METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.
RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.
DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.

Matched MeSH terms: Voice Disorders/physiopathology