Silvana Cunha Costa

Pathological voice discrimination using cepstral analysis, vector quantization and Hidden Markov Models

Pathological voice discrimination has been made using digital signal processing techniques as a complementary tool to videolaringoscopy exams. This method is non-invasive to patients compared to laringoscopy. This paper aims at analyzing the use of cepstral analysis to discriminate voices affected by vocal fold pathologies. A Vector Quantizer using a distortion measurement followed by a Hidden Markov Model-based classifier is employed. Results obtained show an effective and objective way in analyzing voice disorders caused by a vocal fold pathology.

Parametric cepstral analysis for pathological voice assessment

Traditional methods to diagnose laryngeal pathologies such as laryngoscopy are considered invasive and uncomfortable. Methods based on acoustic analisys of speech signals have been investigated in order to diminish the number of laryngoscopical exams. Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the non-invasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscopy. The degree of reliability and effectiveness of discriminating process of pathological voices from normal ones depends on the characteristics and parameters of voice used to train the employed classifier. This paper aims at evaluating the performance of the Linear Prediction Coding (LPC)-based cepstral analysis to discriminate pathological voices of speakers affected by vocal fold edema. For this purpose, LPC, cepstral, weighted cepstral, delta cepstral weighted delta cepstral mel-cepstral coefficients and are used. A vector-quantizing-trained distance classifier is used in the discrimination process.

Feature Estimation for Vocal Fold Edema Detection Using Short-Term Cepstral Analysis

Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the noninvasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscope exams. The acoustic modeling of pathological voices is very important to discriminate normal and pathological voices. The degree of reliability and effectiveness of the discriminating process depends on the appropriate acoustic feature extraction. This paper aims at specifying and evaluating the acoustic features for vocal fold edema through a parametric modeling approach based on the resonant structure of the human speech production mechanism, and a nonparametric approach related to human auditory perception system. For this purpose, LPC and LPC-based cepstral coefficients, and mel-frequency cepstral coefficients are used. A vector-quantizing-trained distance classifier is used in the discrimination process.

Combining entropy measurements and cepstral analysis for pathological voice assessment

This work aims at investigating the combination of classifiers based on three different entropy measurements (Shannon, Relative and Tsallis) and four cepstral coefficients (cepstral, delta-cepstral, weighted cepstral and weighted delta cepstral) to discriminate pathological voices under Reinkes Edema from normal ones. The performance of the combined classifiers is evaluated considering the average and product rules. Classification accuracy is improved when combinations of acoustic features were considered, compared to individual classifiers results.

LPC modelling and cepstral analysis applied to vocal fold pathology detection

Laryngeal pathologies are generally diagnosed using laryngoscopical exams, which are considered invasive to patients. Digital signal processing techniques are noninvasive and can be applied to perform an acoustic analysis for vocal quality assessment providing an objective diagnosis of pathological voices. This paper aims at specifying and evaluating the acoustic features for vocal fold edema through a parametric modelling based on the resonant structure of the human speech production mechanism by LPC and LPC-based cepstral coefficients and a nonparametric approach related to human auditory perception system by mel-frequency cepstral coefficients. A vector-quantising-trained distance classifier is used in the discrimination process.

Pathological voice assessment by recurrence quantification analysis

In this paper, the performance of quantification measures of recurrence plots is evaluated in the task of discriminating pathological voices from the healthy ones. In order to classify these signals as healthy, edema or paralysis, seven recurrence quantification measures are used: Determinism (DET), Maximum length of the diagonal structures (Lmax), Entropy (ENTR), Slope of line-of-best-fit (TREND), Laminarity (LAM), Length of longest vertical line segment (Vmax), and mean vertical line length or trapping time (TT). A discriminant analysis method is applied to each feature individually, using two discriminant functions: Linear and quadratic. The performance of individual classifiers are improved when combining the measures two by two. Results show that the method employed can be used as a highly reliable method for pathological voice assessment.

On the use of cepstral coefficients and Multilayer Perceptron Networks for vocal fold edema diagnosis

Laryngeal diseases affect many professionals who use their voices as the main working tool, such as teachers, singers, radio and TV presenters, among others. Advanced diagnosis techniques of these diseases are typically invasive, causing much discomfort to the patient. In recent years techniques of digital voice processing have been investigated to obtain non-invasive systems to aid the diagnosis by a specialist. This work proposes a method of analysis that employs cepstral coefficients to represent the voice signals, and multilayer perceptron neural networks for discrimination among normal voice, voices affected by local fold Edema and voices affected by other pathologies (nodules, cysts and paralysis). An experimental evaluation of the method has demonstrated that this is a promising approach to the problem, reaching a correct classification rate above 99% for normal voice, 96% for Edema and 93% for other pathologies.

Pathological voice discrimination based on entropy measurements

Digital signal processing techniques have been used to analyze vocal disorders caused by laryngeal pathologies. Laryngoscopical exams commonly used for detection of these diseases are invasive methods that cause discomfort to the patients. Because of its noninvasive nature, acoustical analysis of the temporal and spectral features of the voice signal can be used as an auxiliary technique in laringoscopical exams. This analysis may be applied for detection of vocal diseases and the evaluation of the vocal quality of patients subjected to surgical processes or medical treatments in the vocal folds. This work aims at investigating the behavior of entropy measures in disordered voices influenced by the presence of pathologies in the vocal folds. For this purpose, the Shannon entropy and the relative entropy are implemented, and their behavior for normal and pathological voices affected by vocal fold edemas is observed. The measurement of the entropy efficiency in discriminating between normal and pathological voices is obtained.

Exploiting nonlinearity of the speech production system for voice disorder assessment by recurrence quantification analysis

This work summarizes the research related to digital speech signal processing with recurrence quantification analysis (RQA) applied to voice disorder assessment. The main motivation for these studies is the fact that RQA is able to exploit the nonlinear dynamical nature of the speech production system. Due to the use of recurrence quantification measures to represent the behavior of speech signals, promising results were obtained in the characterization and classification of laryngeal pathologies and voice disorders. These contributions may help one to evaluate the usability and efficiency of RQA in vocal disorder assessment.

Análise da dinâmica do sistema de produção vocal na presença de patologias da laringe

Este trabalho trata da avaliacao do uso de medidas obtidas a partir da analise dinâmica nao linear na discriminacao entre vozes saudaveis e vozes afetadas por patologias laringeas (edemas de Reinke, nodulos e paralisia nas pregas vocais). As medidas empregadas, obtidas por meio da analise de quantificacao de recorrencia, sao: transitividade, comprimento medio das linhas diagonais e o tempo de recorrencia dos tipos 1 e 2. Os resultados obtidos no processo de classificacao indicam que as medidas avaliadas apresentam potencial discriminativo entre os dois grupos de sinais de voz considerados, podendo ser empregadas em sistemas de auxilio ao diagnostico de patologias na laringe.