An improved method for voice pathology detection by means of a HMM-based feature space transformation

Arias-Londoño, Julián D.; Godino-Llorente, Juan Ignacio; Sáenz-Lechón, Nicolás; Osma-Ruiz, Víctor; Castellanos-Domínguez, Germán

doi:10.1016/j.patcog.2010.03.019

Cited by 66 publications

(26 citation statements)

References 43 publications

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“…The results were evaluated using Classification Error Rate (CER), Accuracy (Acc) and Cumulative Error (Arias-Londoño et al, 2010;Godino et al, 2005Godino et al, , 2006. For the training and validation steps, we used k-fold cross-validation with k = 10.…”

Section: Automatic Classificationmentioning

confidence: 99%

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

López-de-Ipiña

Solé-Casals

Eguiraun

et al. 2015

Computer Speech & Language

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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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Section: Automatic Classificationmentioning

confidence: 99%

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

López-de-Ipiña

Solé-Casals

Eguiraun

et al. 2015

Computer Speech & Language

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“…Automated systems have been used to detect the presence of pathological voice, specifically vocal fold (i.e., laryngeal) disorders (Arias-Londoñ o et al, 2010;Fraile et al, 2009;Szaleniec et al, 2007;Wielgat et al, 2008), and assess the intelligibility of subjects with dysglossia and dysphonia to assist in rehabilitation (Maier et al, 2010). In these disorders, the speech production organs are affected, which results in atypicalities in the voice.…”

Section: Speech Technology Tools In Disordered Voice and Speech Therapymentioning

confidence: 99%

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Shahin

Ahmed

Parnandi

et al. 2015

Speech Communication

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“…Publications [16]- [20] utilized PdA (Príncipe de Asturias) database. Reference [16] used MFCC, Noise and energy features with HMM classifier to detect pathological speech giving accuracy of 82.14%.…”

Section: Literature Studymentioning

confidence: 99%

“…Reference [16] used MFCC, Noise and energy features with HMM classifier to detect pathological speech giving accuracy of 82.14%. SVM classifier with MFCC and modulation spectra based features giving accuracy of 81.70% is reported [17].…”

Section: Literature Studymentioning

confidence: 99%

Feature enhancement for classifier optimization and dimensionality reduction

Shilaskar¹,

Ghatol²

2014

2014 Annual IEEE India Conference (INDICON)

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Voice is important for professionals like speakers, teachers, actors, singers and it is the important tool for communication. Laryngeal pathologies induce perturbations in the speech signal. Speech signal is discriminated as pathological or healthy based on roughness -breathiness -hoarseness (RBH) in the quality of signal. In recent years pattern recognition along with various signal processing techniques has emerged as an effective non invasive tool for diagnosis of pathological condition. Signal processing techniques tend to generate large number of features representing the signal. Automatic feature reduction techniques are vital in identifying the relevant features and eliminating the redundant ones. We extract features from speech signal using the acoustic analysis. Features are enhanced by alleviating gender bias. Periodic variations in the signal are captured using statistical techniques. We investigate intelligent system to generate reduced feature subset with improvement in diagnostic performance.

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An improved method for voice pathology detection by means of a HMM-based feature space transformation

Cited by 66 publications

References 43 publications

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

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

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Feature enhancement for classifier optimization and dimensionality reduction

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