A Study of Speech Fractal Dimensions

Alzheimer's disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by using automatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selected is based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work is feature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. The feature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters in the feature vector in order to enhance the performance of the original system while controlling the computational cost. © 2014 Elsevier Ltd. All rights reserved. E-mail addresses: karmele.ipina@ehu.es (K. López-de-Ipiña), jordi.sole@uvic.cat (J. Solé-Casals), harkaitz.eguiraun@ehu.es (H. Eguiraun), jesus.alonso@ulpgc.es (J.B. Alonso), carlos.travieso@ulpgc.es (C.M. Travieso), aitzol.ezeiza@ehu.es (A. Ezeiza), nora.barroso@ehu.es (N. Barroso), mecay@cita-alzheimer.org (M. Ecay-Torres), pmlage@cita-alzheimer.org (P. Martinez-Lage), mariablanca.beitia@ehu.es (B. Beitia).

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Effects of Low-Pass Filtering on Acoustic Analysis of Voice

MacCallum

Olszewski

Jiang

2011

Journal of Voice

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Objective/Hypothesis-Low-pass filtering is often applied to eliminate effects of environmental noise when preparing voice recordings for acoustic analysis. This study tested the effects of low-pass filter cutoff frequency on the results of acoustic voice analysis, with a particular interest in the effects of low cutoff frequencies on nonlinear dynamic parameters.Study Design-A crossover randomized controlled trial was performed using voice recordings of sustained vowel phonation obtained from the Disordered Voice Database.Methods-A second-order Butterworth filter was applied to the voices at cutoff frequencies ranging from 5000 to 40Hz. Percent jitter, percent shimmer, fundamental frequency (F 0 ), signalto-noise ratio (SNR), D 2 , and K 2 were calculated for each signal.Results-Traditional acoustic parameters were validly measured at cutoff frequencies as low as 300Hz. SNR and percent shimmer were improved by cutoff frequencies of 300Hz or higher; F 0 and percent jitter were unaffected by filtering at these frequencies. D 2 and K 2 were measured stably for signals filtered at cutoff frequencies as low as 100Hz.Conclusion-To ensure accuracy in acoustic voice analysis, setting the cutoff frequency of a low-pass filter at least one octave above the fundamental frequency (minimum of 300Hz) is recommended. Nonlinear dynamic measures of correlation dimension (D 2 ) and second-order entropy (K 2 ) proved more robust and maintained accuracy at lower frequencies.

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A Study of Speech Fractal Dimensions

Cited by 7 publications

References 14 publications

A Fractal Approach of Pathological Voices

A Fractal Approach of Pathological Voices

Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach

Effects of Low-Pass Filtering on Acoustic Analysis of Voice

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