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Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Tec...
Fernandes, Joana Filipa Teixeira; Freitas, Diamantino Rui; Teixeira, João PauloUsing acoustic analysis to classify and identify speech disorders noninvasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread ...
Determination of harmonic parameters in pathological voices-efficient algorithm
Fernandes, Joana Filipa Teixeira; Freitas, Diamantino Silva; Candido Junior, Arnaldo; Teixeira, João PauloFeatured Application The paper describes a low-complexity/efficient algorithm to determine the short-term Autocorrelation, HNR, and NHR in sustained vowel audios, to be used in stand-alone devices with low computational power. These parameters can be used as input features of a smart medical decision support system for speech pathology diagnosis. The harmonic parameters Autocorrelation, Harmonic to Noise Ratio ...
Optimization of glottal onset peak detection algorithm for accurate Jitter meas...
Fernandes, Joana Filipa Teixeira; Borghi, Pedro Henrique; Freitas, Diamantino Silva; Teixeira, João PauloJitter is an acoustic parameter used as input for intelligent systems for the diagnosis of speech related pathologies. This work has the objective to improve an algorithm that allows to extract vocal parameters, and thus improve the accuracy measurement of absolute jitter parameter. Some signals were analyzed, where signal to signal was compared in order to try to understand why the values are different in some...
Transfer learning with audioSet to voice pathologies identification in continuo...
Guedes, Victor; Teixeira, Felipe; Oliveira, Alessa Anjos de; Fernandes, Joana Filipa Teixeira; Silva, Letícia; Candido Junior, ArnaldoThe 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...
Determinação da autocorrelação, HNR e NHR para análise acústica vocal
Fernandes, Joana Filipa TeixeiraEste trabalho teve como objetivo a determinação dos parâmetros: Harmonic to Noise Ration (HNR), Noise to Harmonic Ratio (NHR) e Autocorrelação. Estes parâmetros são usados como entradas de um sistema inteligente para diagnóstico de patologias da fala. Foi realizada uma análise comparativa entre os valores do algoritmo e do software Praat, de modo a perceber qual a melhor janela e o seu comprimento, em número de...
Parameters for vocal acoustic analysis - cured database
Fernandes, Joana Filipa Teixeira; Silva, Letícia; Teixeira, Felipe; Guedes, Victor; Santos, Juliana Hermsdorf; Teixeira, João PauloThis 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 jitt...
Outliers treatment to improve the recognition of voice pathologies
Silva, Letícia; Hermsdorf, Juliana; Guedes, Victor; Teixeira, Felipe; Fernandes, Joana Filipa Teixeira; Bispo, Bruno; Teixeira, João PauloIn 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 tre...
Classification of control/pathologic subjects with support vector machines
Teixeira, Felipe; Fernandes, Joana Filipa Teixeira; Guedes, Victor; Candido Junior, Arnaldo; Teixeira, João PauloThe 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 fro...
Acoustic analysis of chronic laryngitis - statistical analysis of sustained spe...
Teixeira, João Paulo; Fernandes, Joana Filipa Teixeira; Teixeira, Felipe; Fernandes, Paula OdeteThis 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...
Long short term memory on chronic laryngitis classification
Guedes, Victor; Candido Junior, Arnaldo; Fernandes, Joana Filipa Teixeira; Teixeira, Felipe; Teixeira, João PauloThe 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...