Utilização de ferramentas de machine learning no diagnóstico de patologias da laringe

Este trabalho está relacionado com o estudo e utilização de um conjunto de ferramentas de machine learning, nomeadamente árvores de decisão, support vector machines (SVM’s), Deep-learning - Deep Neural Networks, com o prepósito de fazer a classificação entre fala patológica e fala normal, e identificar a patologia com estas ferramentas. As patologias utilizadas neste estudo são a laringite crónica, disfonia e paralisia das cordas vocais. Utilizou-se a base de dados Alemã Saarbrucken Voice Database (SVD), que se encontra disponível online de forma gratuita pelo Instituto de Fonética da Universidade de Saarland. Nesta base de dados é possível encontrar sinais de voz, entre saudáveis e patológicos de mais de 2000 sujeitos. Foram utilizados três grupos de parâmetros, o grupo I (a), contêm parâmetros como Jitter relativo, Shimmer relativo e Harmonic to Noise Ratio (HNR), determinados em segmentos de fala estacionária, onde se atingiu 80.7% de exatidão para distinguir saudáveis e patológicos com SVM. O grupo I (b), contêm os parâmetros do grupo I(a), Noise to Harmonic Ratio (NHR) e Autocorrelação determinados em segmentos de fala estacionária, onde se atingiu 79.2% de exatidão para distinguir saudáveis e patológicos com SVM. O grupo II é baseado em Mel Frequency Cepstral Coefficientes (MFCC’s), determinados nos segmentos de fala estacionários, onde se atingiu 83.3% de exatidão para distinguir saudáveis e laringite com SVM. O grupo III é formado por coeficientes MFCC’s extraídos de fala contínua onde se atingiu 71% de exatidão para distinguir saudáveis e patológicos com Redes Neuronais. Realizou-se uma análise estatística referente aos parâmetros do grupo I (b), com o propósito de identificar características únicas em determinados parâmetros, que permitissem diferenciar as patologias. No decorrer deste trabalho, embora não fosse objetivo inicial, deu-se início a elaboração de um “software” protótipo para fazer gravação de voz, extração de parâmetros e classificação da patologia.This work is related to the study and use of a set of machine learning tools, namely decision trees, Support Vector Machines (SVM's), Deep learning - Deep Neural Networks (neural networks), with the purpose of classifying speech pathological and normal speech, and to identify the pathology with these tools. The pathologies used in this study are chronic laryngitis, dysphonia and vocal cord paralysis. We use the database of the German Saarbrucken Voice Database (SVD), which is available online for free at the Institute of Phonetics at the University of Saarland. In this database it is possible to find voice signals between healthy and pathological of more than 2000 subjects. Three groups of parameters were used, the first one is the group I (a) contains parameters such as Relative Jitter, Relative Shimmer and Harmonic to Noise Ratio (HNR), determined in stationary speech segments, where 80.7% accuracy was achieved to distinguish healthy and pathologies. The group I (b), contain parameters like Relative Jitter, Relative Shimmer, HNR, Noise to Harmonic Ratio (NHR) and Autocorrelation determined in segments of stationary speech, where it obtained 79.2% accuracy to distinguish healthy and pathological patients with SVM. Group II is based on Mel Frequency Cepstral Coefficients (MFCC's), determined in stationary speech segments, where it obtained 83.3% accuracy to distinguish the healthy and laryngitis with SVM. Group III is formed by MFCC coefficients, extracted from continuous speech, where it reached 71% of accuracy to distinguish healthy and pathologies with Neuronal Networks. The statistical study concerning the parameters of group I (b) was performed, in which three 'a', 'i' and 'u' vowels were analyzed in three differents tones: high, low and normal. The statistical study was performed with the purpose of detecting unique characteristics in certain parameters, which allowed to distinguish the pathologies used in this dissertation. In the course of this work, although it was not an initial objective, Started the development of prototype software to make voice recording, parameter extraction and classification of the pathology.Este trabajo está relacionado con el estudio y utilización de un conjunto de herramientas de machine learning, dígase árboles de decisión, support vector machines, Deep learning- Deep Neural Networks (redes neuronales), con el propósito de hacer la clasificación entre habla patológica y habla normal e identificar la patología con estas herramientas. Las patologías utilizadas en este estudio son la laringitis crónica, disfonía y parálisis de las cuerdas vocales. Se ha utilizado la base de datos alemana Saarbrucken Voice Database (SVD), que se encuentra disponible online de forma gratuita por el Instituto de Fonética de la Universidad de Saarland. En esta base de datos es posible encontrar señales de voz, entre saludables y patológicos de más de 2000 sujetos. Se han analizado tres grupos de parámetros, el grupo I(a) contiene parámetros como Jitter relativo, Shimmer relativo, HNR, determinados en segmentos de habla estacionaria, alcanzaron una precisión del 80.7% para distinguir entre sano y patológico. El grupo I(b), contiene parámetros como Jitter relativo, Shimmer relativo, HNR, NHR y Autocorrelación, determinados en segmentos de habla estacionaria, donde se alcanzó una precisión del 79,2% para distinguir sanos y patológicos con la SVM. O grupo II está basado en coeficientes MFCC’s, determinados en segmentos de habla estacionaria, donde se logró una precisión del 83.3% para distinguir los sanos y la laringitis con SVM. El grupo III está formado por coeficientes MFCC extraídos del habla continua, que alcanzaron el 71% de precisión para distinguir los sanos y patológicas con Redes neuronales. Se ha realizado el estudio estadístico referente a los parámetros del grupo I(b), cuyas 3 vocales “a”, “i” y “u” en tres tonos disponibles alto, bajo y normal fueron analizadas. el estudio estadístico se ha realizado con el propósito de detectar características únicas en determinados parámetros, que permitieran diferenciar las patologías utilizadas en esta disertación. En el transcurso de este trabajo, aunque no fuera el objetivo inicial, se dió inicio a la elaboración de un “software” prototipo para hacer grabación de voz, extracción de parámetros y clasificación de la patología

Cured database of sustained speech parameters for chronic laryngitis pathology

This paper reports the construction and organization of a database of speech parameters extracted from a speech sound database. The database is freely available on internet and the paper intends also theirs advertise for the research community. The database includes the parameters extracted from the sound of sustained vowels produced by a group of Chronic Laryngitis patients and a group of control subjects with similar characteristics concerning gender and age. The set of parameters of this database consists in the Jitter, Shimmer, Harmonic to Noise Ratio (HNR), Noise to Harmonic Ratio (NHR) and Autocorrelation extracted from the sound of sustained vowels /a/, /i/ and /u/ at low, neutral and high tones.info:eu-repo/semantics/publishedVersio

Acoustic analysis of chronic laryngitis - statistical analysis of sustained speech parameters

This paper describes the statistical analysis of a set of features extracted from the speech of sustained vowels of patients with chronic laryngitis and control subjects. The idea is to identify which features can be useful in a classification intelligent system to discriminate between pathologic and healthy voices. The set of features analysed consist in the Jitter, Shimmer Harmonic to Noise Ratio (HNR), Noise to Harmonic Ratio (NHR) and Autocorrelation extracted from the sound of a sustained vowels /a/, /i/ and /u/ in a low, neutral and high tones. The results showed that besides the absolute Jitter, no statistical significance exist between male and female voices, considering the classification between pathologic or healthy. Any of the analysed parameters is likely to be a statistical difference between control and Chronic Laryngitis groups. This is an important information that these features can be used in an intelligent system to classify healthy from Chronic Laryngitis voices.info:eu-repo/semantics/publishedVersio

Classification of control/pathologic subjects with support vector machines

The diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.info:eu-repo/semantics/publishedVersio

Long short term memory on chronic laryngitis classification

The classification study with the use of machine learning concepts has been applied for years, and one of the aspects in which this can be applied is for the analysis of speech acoustics applied to the analysis of pathologies. Among the pathologies present, one of them is chronic laryngitis. Thus, this article aims to present the results for a classification of chronic laryngitis with the use of Long Short Term Memory as a classifier. The parameters of relative jitter, relative shimmer and autocorrelation was used as input of the LSTM. A dataset of about 1500 instances were used to train, validate and test along 4 experiments with LSTM and one feedforward Artificial Neural Network (ANN). The results of the LSTM overcome the ones of the feedforward ANN, and was about 100% accuracy, sensitivity and specificity in test set, denoting a promising future for this classification tool in the voice pathologies diagnose.info:eu-repo/semantics/publishedVersio

Harmonic to noise ratio measurement - selection of window and length

Harmonic to Noise Ratio (HNR) measures the ratio between periodic and non-periodic components of a speech sound. It has become more and more important in the vocal acoustic analysis to diagnose pathologic voices. The measure of this parameter can be done with Praat software that is commonly accept by the scientific community has an accurate measure. Anyhow, this measure is dependent with the type of window used and its length. In this paper an analysis of the influence of the window and its length was made. The Hanning, Hamming and Blackman windows and the lengths between 6 and 24 glottal periods were experimented. Speech files of control subjects and pathologic subjects were used. The results showed that the Hanning window with the length of 12 glottal periods gives measures of HNR more close to the Praat measures.info:eu-repo/semantics/publishedVersio

Parameters for vocal acoustic analysis - cured database

This paper describes the construction and organization of a database of speech parameters extracted from a speech database. This article intends to inform the community about the existence of this database for future research. The database includes parameters extracted from sounds produced by patients distributed among 19 diseases and control subjects. The set of parameters of this database consists of the jitter, shimmer, Harmonic to Noise Ratio (HNR), Noise to Harmonic Ratio (NHR), autocorrelation and Mel Frequency Cepstral Coefficients (MFCC) extracted from the sound of sustained vowels /a/, /i/ and /u/ at the high, low and normal tones, and a short German sentence. The cured database has a total number of 707 pathological subjects (distributed by the various diseases) and 194 control subjects, in a total of 901 subjects.info:eu-repo/semantics/publishedVersio

Outliers treatment to improve the recognition of voice pathologies

In some of the processes used in data analysis, such as the recognition of pathologies and pathological subjects, the presence of anomalous instances in the dataset is an unfavorable situation that can lead to misleading results. This article presents a function that implements the identification of anomalies in dataset using the boxplot and standard deviation methods. Also was used the filling technique to treat these anomalies, in which the anomalous point value were substituted by a limit value determined by the boxplot or standard deviation methods. To improve the outliers methods some normalization processes based on the z-score, logarithmic and squared root methodologies were experimented. These outliers treatment were applied to the dataset used in the recognition of vocal pathologies (dysphonia, chronic laryngitis and vocal cords paralysis vs control), performed by a MLP and LSTM neural networks. After the experiments, both the standard deviation and the boxplot methods with z-score normalization showed very useful for pre-processing the dataset for voice pathologies recognition. The accuracy was improved between 3 and 13 points in percentage.info:eu-repo/semantics/publishedVersio

Transfer learning with audioSet to voice pathologies identification in continuous speech

The classification of pathological diseases with the implementation of concepts of Deep Learning has been increasing considerably in recent times. Among the works developed there are good results for the classification in sustained speech with vowels, but few related works for the classification in continuous speech. This work uses the German Saarbrücken Voice Database with the phrase “Guten Morgen, wie geht es Ihnen?” to classify four classes: dysphonia, laryngitis, paralysis of vocal cords and healthy voices. Transfer learning concepts were used with the AudioSet database. Two models were developed based on Long-Short-Term-Memory and Convolutional Network for classification of extracted embeddings and comparison of the best results, using cross-validation. The final results allowed to obtaining 40% of f1-score for the four classes, 66% f1-score for Dysphonia x Healthy, 67% for Laryngitis x healthy and 80% for Paralysis x Healthy.info:eu-repo/semantics/publishedVersio

Treatment of class II furcation defects with autogenous bone graft associated with Bichat’s fat pad: case report

The periodontal treatment of teeth with furcation defect is clinically challenging. In cases of class II furcation defects, the regenerative surgery shows low morbidity and good prognosis when correctly indicated. The aim of the presentstudy is to report a treatment option for class II furcation defect through autogenous bone graft associated with the Bichat’s fat pad. Case report: A 59-year-old female patient was diagnosed with class II furcation defect in the left mandibular first molar. The treatment comprised surgical reconstruction of the defect with a combination of maxillary tuberosity bone graft and Bichat’s fat pad. The clinical and radiographic follow-up of 180 days showed bone formation inthe furcation area and absence of probing depth. Conclusion: An association of autogenous graft form the maxillary tuberosity with a Bichat’s fat pad proved to be a safe, low cost, and effective therapy for the regenerative treatment of class II furcation.The periodontal treatment of teeth with furcation defect is clinically challenging. In cases of class II furcation defects, the regenerative surgery shows low morbidity and good prognosis when correctly indicated. The aim of the presentstudy is to report a treatment option for class II furcation defect through autogenous bone graft associated with the Bichat’s fat pad. Case report: A 59-year-old female patient was diagnosed with class II furcation defect in the left mandibular first molar. The treatment comprised surgical reconstruction of the defect with a combination of maxillary tuberosity bone graft and Bichat’s fat pad. The clinical and radiographic follow-up of 180 days showed bone formation inthe furcation area and absence of probing depth. Conclusion: An association of autogenous graft form the maxillary tuberosity with a Bichat’s fat pad proved to be a safe, low cost, and effective therapy for the regenerative treatment of class II furcation