Feature analysis for automatic detection of pathological speech

Dibazar, Alireza A.; Narayanan, Shrikanth; Berger, Theodore W.

doi:10.1109/iembs.2002.1134447

Cited by 102 publications

(79 citation statements)

References 2 publications

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“…The work presented in this paper extends previously reported results in [9]. We are not interested in the detection of pathological speech as in [7], but in the assessment of the discriminatory capability of the aforementioned classifiers for detecting vocal fold paralysis in male speakers and the detection of vocal fold edema in female speakers. The pattern recognition experiments were conducted by employing either frame-based 14th order linear prediction coefficients or their long-term mean vectors for each speaker.…”

Section: Introductionsupporting

confidence: 62%

“…Closely related previous works are the detection of vocal fold cancer [6], where a Hidden Markov Model (HMM)-based classifier was employed and the binary classification between normal subjects and subjects suffering from different pathologies in [7], where Mel frequency cepstral coefficients and pitch were used as features for classification that was performed by the linear discriminant classifier, the nearest mean classifier, and classifiers based on Gaussian mixture models or HMMs. Three parameters namely the number of discrimination, the level of clustering, and the average clustering were assessed for disease discrimination based on acoustic features in [8].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Vocal Fold Paralysis and Edema Using Linear Discriminant Classifiers

Ziogas

Kotropoulos

2006

Advances in Artificial Intelligence

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Abstract. In this paper, a two-class pattern recognition problem is studied, namely the automatic detection of speech disorders such as vocal fold paralysis and edema by processing the speech signal recorded from patients affected by the aforementioned pathologies as well as speakers unaffected by these pathologies. The data used were extracted from the Massachusetts Eye and Ear Infirmary database of disordered speech. The linear prediction coefficients are used as input to the pattern recognition problem. Two techniques are developed. The first technique is an optimal linear classifier design, while the second one is based on the dual-space linear discriminant analysis. Two experiments were conducted in order to assess the performance of the techniques developed namely the detection of vocal fold paralysis for male speakers and the detection of vocal fold edema for female speakers. Receiver operating characteristic curves are presented. Long-term mean feature vectors are proven very efficient in detecting the voice disorders yielding a probability of detection that may approach 100% for a probability of false alarm equal to 9.52%.

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Section: Introductionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Detection of Vocal Fold Paralysis and Edema Using Linear Discriminant Classifiers

Ziogas

Kotropoulos

2006

Advances in Artificial Intelligence

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“…In (Dibazar, Narayanan & Berger, 2002), more complex probabilistic models, such as HMM have also been used for voice pathology detection reported different accuracies ranging from 97.75% to 98.3%. The features used in these cases are MFCC, the velocity and acceleration parameters, as well as different acoustic and noise measures.…”

Section: Related Workmentioning

confidence: 99%

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

Saudi¹,

Youssif²,

Ghalwash³

2012

CIS

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In the context of the recognition of vocal folds disorders, the systems based on acoustic analysis are being introduced as computer aided medical diagnosis tools due to its objectivity and noninvasive nature. Acoustic analysis is a complementary tool to those methods based on direct observation of the vocal folds by laryngoscopy; also, it can be used for the evaluation of surgical operation. This paper presents a novel approach in voice pathology assessment using RASTA-PLP feature extraction method in the framework of a HMM. The proposed method then compared to other feature extraction methods such as MFCC and PLP. The experimental results show that RASTA-PLP attained 92.86% correct classification rates and AUC of 0.94 compared to 0.81 and 0.79 for MFCC and PLP respectively.

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“…Most of the current methods used for evaluating Parkinson's disease (PD) rely heavily on human expertise . In diagnosis of PD literature, there have been extensive studies of speech measurement for general voice disorders [5,6] and PD in particular [7,8].In these studies, the speech sounds produced during standard speech tests are recorded using a microphone, and the recorded speech signals are subsequently analyzed using measurement methods implemented in software algorithms) designed to detect certain properties of these signals. Mel-frequency cepstral coefficients (MFCC) have traditionally been used in speaker identification applications.…”

Section: Introductionmentioning

confidence: 99%

Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization

Kapoor¹,

Sharma²

2011

IJCA

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ABSTRACT:This paper investigates the adaptation of MFCCs to the diagnosis of Parkinson's disease (PD). The aim of this study is to provide a novel method, suitable for keeping track of the evolution of the patient's pathology: easy-to-use, fast, non-invasive for the patient, and affordable for the clinicians. This method will be complementary to the existing ones -the perceptual judgment and the usual objective measurement (jitter, airflows...) which remain time and human resource consuming. The system designed for this particular task relies on the Mel-Frequency Cepstral coefficients (MFCC) for feature extraction and Vector Quantization (VQ) for feature analysis which is the state-of-theart for speaker recognition.

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Feature analysis for automatic detection of pathological speech

Cited by 102 publications

References 2 publications

Detection of Vocal Fold Paralysis and Edema Using Linear Discriminant Classifiers

Detection of Vocal Fold Paralysis and Edema Using Linear Discriminant Classifiers

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization

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