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

Voice pathologies : the most comum features and classification tools

Speech pathologies are quite common in society, however the exams that exist are invasive, making them uncomfortable for patients and depending on the experience of the clinician who performs the assessment. Hence the need to develop non-invasive methods, which allow objective and efficient analysis. Taking this need into account in this work, the most promising list of features and classifiers was identified. As features, jitter, shimmer, HNR, LPC, PLP, and MFCC were identified and as classifiers CNN, RNN and LSTM. This study intends to develop a device to support medical decision, however this article already presents the system interface.info:eu-repo/semantics/publishedVersio

Deep-learning in identification of vocal pathologies

The work consists in a classification problem of four classes of vocal pathologies using one Deep Neural Network. Three groups of features extracted from speech of subjects with Dysphonia, Vocal Fold Paralysis, Laryngitis Chronica and controls were experimented. The best group of features are related with the source: relative jitter, relative shimmer, and HNR. A Deep Neural Network architecture with two levels were experimented. The first level consists in 7 estimators and second level a decision maker. In second level of the Deep Neural Network an accuracy of 39,5% is reached for a diagnosis among the 4 classes under analysis.info:eu-repo/semantics/publishedVersio

Automated Voice Pathology Discrimination from Continuous Speech Benefits from Analysis by Phonetic Context

In contrast to previous studies that look only at discriminating pathological voice from the normal voice, in this study we focus on the discrimination between cases of spasmodic dysphonia (SD) and vocal fold palsy (VP) using automated analysis of speech recordings. The hypothesis is that discrimination will be enhanced by studying continuous speech, since the different pathologies are likely to have different effects in different phonetic contexts. We collected audio recordings of isolated vowels and of a read passage from 60 patients diagnosed with SD (N=38) or VP (N=22). Baseline classifiers on features extracted from the recordings taken as a whole gave a cross-validated unweighted average recall of up to 75% for discriminating the two pathologies. We used an automated method to divide the read passage into phone-labelled regions and built classifiers for each phone. Results show that the discriminability of the pathologies varied with phonetic context as predicted. Since different phone contexts provide different information about the pathologies, classification is improved by fusing phone predictions, to achieve a classification accuracy of 83%. The work has implications for the differential diagnosis of voice pathologies and contributes to a better understanding of their impact on speech

Automated voice pathology discrimination from audio recordings benefits from phonetic analysis of continuous speech

In this paper we evaluate the hypothesis that automated methods for diagnosis of voice disorders from speech recordings would benefit from contextual information found in continuous speech. Rather than basing a diagnosis on how disorders affect the average acoustic properties of the speech signal, the idea is to exploit the possibility that different disorders will cause different acoustic changes within different phonetic contexts. Any differences in the pattern of effects across contexts would then provide additional information for discrimination of pathologies. We evaluate this approach using two complementary studies: the first uses a short phrase which is automatically annotated using a phonetic transcription, the second uses a long reading passage which is automatically annotated from text. The first study uses a single sentence recorded from 597 speakers in the Saarbrucken Voice Database to discriminate structural from neurogenic disorders. The results show that discrimination performance for these broad pathology classes improves from 59% to 67% unweighted average recall when classifiers are trained for each phone-label and the results fused. Although the phonetic contexts improved discrimination, the overall sensitivity and specificity of the method seems insufficient for clinical application. We hypothesise that this is because of the limited contexts in the speech audio and the heterogeneous nature of the disorders. In the second study we address these issues by processing recordings of a long reading passage obtained from clinical recordings of 60 speakers with either Spasmodic Dysphonia or Vocal fold Paralysis. We show that discrimination performance increases from 80% to 87% unweighted average recall if classifiers are trained for each phone-labelled region and predictions fused. We also show that the sensitivity and specificity of a diagnostic test with this performance is similar to other diagnostic procedures in clinical use. In conclusion, the studies confirm that the exploitation of contextual differences in the way disorders affect speech improves automated diagnostic performance, and that automated methods for phonetic annotation of reading passages are robust enough to extract useful diagnostic information

An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification

Background and Objective Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. Materials and Methods Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. Results The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively

Spectral analysis of pathological acoustic speech waveforms

Biomedical engineering is the application of engineering principles and techniques to the medical field. The design and problem solving skills of engineering are combined with medical and biological science, which improves medical disorder diagnosis and treatment. The purpose of this study is to develop an automated procedure for detecting excessive jitter in speech signals, which is useful for differentiating normal from pathologic speech. The fundamental motivation for this research is that tools are needed by speech pathologists and laryngologists for use in the early detection and treatment of laryngeal disorders. Acoustical analysis of speech was performed to analyze various features of a speech signal. Earlier research established a relation between pitch period jitter and harmonic bandwidth. This concept was used for detecting laryngeal disorders in speech since pathologic speech has been found to have larger amounts of jitter than normal speech. Our study was performed using vowel samples from the voice disorder database recorded at the Massachusetts Eye and Ear Infirmary (MEEI) in1994. The KAYPENTAX company markets this database. Software development was conducted using MATLAB, a user-friendly programming language which has been applied widely for signal processing. An algorithm was developed to compute harmonic bandwidths for various speech samples of sustained vowel sounds. Open and closed tests were conducted on 23 samples of pathologic and normal speech samples each. Classification results showed 69.56% probability of correct detection of pathologic speech samples during an open test

Identification of voice pathologies in an elderly population

Ageing is associated with an increased risk of developing diseases, including a greater pre- disposition to develop diseases such as Sepsis. Also, with ageing, human voices undergo a natural degradation gauged by alterations in hoarseness, breathiness, articulatory ability, and speaking rate. Nowadays, perceptual evaluation is widely used to assess speech and voice impairments despite its high subjectivity. This dissertation proposes a new method for detecting and identifying voice patholo- gies by exploring acoustic parameters of continuous speech signals in the elderly popula- tion. Additionally, a study of the influence of gender and age on voice pathology detection systems’ performance is conducted. The study included 44 subjects older than 60 years old, with the pathologies Dyspho- nia, Functional Dysphonia, and Spasmodic Dysphonia. In the dataset originated with these settings, two gender-dependent subsets were created, one with only female samples and the other with only male samples. The system developed used three feature selection methods and five Machine Learning algorithms to classify the voice signal according to the presence of pathology. The binary classification, which consisted of voice pathology detection, reached an accuracy of 85,1%±5,1% for the dataset without gender division, 83,7%±7,0% for the male dataset, and 87,4%±4,2% for the female dataset. As for the multiclass classifica- tion, which consisted of the classification of different pathologies, reached an accuracy of 69,0%±5,1% for the dataset without gender division, 63,7%± 5,4% for the male dataset, and 80,6%±8,1% for the female dataset. The obtained results revealed that features that describe fluency are important and discriminating in these types of systems. Also, Random Forest has shown to be the most effective Machine Learning algorithm for both binary and multiclass classification. The proposed model proves to be promising in detecting pathological voices and identifying the underlying pathology in an elderly population, with an increase in its performance when a gender division is performed.O envelhecimento está associado a um maior risco de desenvolvimento de doenças, nome- adamente a uma maior predisposição para a evolução de doenças como a Sepsis. Inclusiva- mente, com o envelhecimento, a voz sofre uma degradação natural aferindo-se alterações na rouquidão, respiração, capacidade articulatória e no ritmo do discurso. Atualmente, a avaliação percetual é amplamente utilizada para avaliar as perturbações da fala e da voz, possuindo elevada subjetividade. Esta dissertação propõe um novo método de deteção e identificação de patologias da voz através da exploração de parâmetros acústicos de sinais de fala contínua na população idosa. Adicionalmente, é realizado um estudo da influência do género e da idade no desempenho dos sistemas de detecção de patologias da voz. A amostra deste estudo é composta por 44 indivíduos com idades superiores a 60 anos referentes às patologias Disfonia, Disfonia Funcional e Disfonia Espasmódica. No conjunto de dados originados com esta configuração, foram criados dois subconjuntos de- pendentes do género: um com apenas amostras femininas e o outro com apenas amostras masculinas. O sistema desenvolvido utilizou três métodos de seleção de atributos e cinco algoritmos de Aprendizagem Automática de modo a classificar o sinal de voz de acordo com a presença de patologias da voz. A deteção de patologia de voz alcançou uma exatidão de 85,1%±5,1% para os da- dos sem divisão de género, 83,7%±7,0% para os dados masculinos, e 87,4%±4,2% para os dados femininos. A classificação de diferentes patologias alcançou uma exatidão de 69,0%±5,1% para os dados sem divisão de género, 63,7%±5,4% para os dados masculinos, e 80,6%±8,1% para os dados femininos. Os resultados obtidos revelaram que os atributos que caracterizam a fluência são importantes e discriminatórios nestes tipos de sistemas. Ademais, o classificador Random Forest demonstrou ser o algoritmo mais eficaz na deteção e identificação de patologias da voz. O modelo proposto revelou-se promissor na deteção de vozes patológicas e identifi- cação da patologia subjacente numa população idosa, aumentando o seu desempenho quando ocorre uma divisão de género