Sigma-lognormal modeling of speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject’s age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics-based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma-lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR-TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Měřitelné změny hlasu při léčbě poruch hlasu

The main purpose of this paper is to show identification possibilities of voice differences for people whose voice has been influenced by any kind of voice disorder. Introduction of common diseases of vocal cords or larynx is followed by a chapter including ordinary treatment techniques. Even if the surgery ends up well the voice production can be affected in some way. Doctors are mostly able to measure only limited amount of voice characterizing parameters. More precise analysis of subjects within predefined time intervals should lead to more specific results and may prove more efficient. This article presents a different scientific approach which is based on the voice parameterization and analysis. The only thing needed for this kind of research is obtaining of recordings of analyzed subjects (before surgery, soon after that and then for example 2 months later). These recordings can be processed using common audio processing methods and required variables are extracted and saved in form of so-called feature vectors. Some features are expected to change as the result of treatment. Some of used methods are similar to ordinary techniques or they have something in common, but it allows to measure and identify even more variables describing the voice. Diagnostic experience can be supplemented by our software, where many parameters are visualized. But the final decision is still up to the doctor.Hlavním cílem tohoto článku je ukázat možnosti detekce změn hlasu u lidí, jejichž hlas byl ovlivněn poruchou hlasu. Po úvodní části věnované představení běžných onemocnění hlasivek či hrtanu je zařazena kapitola popisující běžné metody a techniky léčby. Avšak i po úspěšné operaci může být proces tvorby hlasu ovlivněn. Lékaři mají typicky k dispozici pouze omezený počet měřených parametrů charakterizujících hlas pacienta. Podrobnější pozorování pacientů v předem definovaných časových intervalech může vést ke zvýšení efektivity a zpřesnění výsledků nejen pro diagnostiku. Článek prezentuje odlišný přístup založený na parametrizaci hlasu a jeho analýze. Při tomto postupu postačí pouze pořídit nahrávky hlasu pacientů (před operací, brzy po operaci a zhruba po 2 měsících od operace). Tyto nahrávky jsou zpracovány s využitím běžných metod pro zpracování řeči a požadované parametry jsou z řeči extrahovány a posléze ukládány ve formě tzv. příznakových vektorů. V průběhu léčby jsou očekávány změny hodnot příznaků. Závěry diagnostiky mohou být podpořeny tímto diagnostickým softwarem, který výsledky analýzy přehledně vizualizuje. Nicméně konečné rozhodnutí je stále v rukou lékaře

Exploratory Matrix Factorization Techniques for Large Scale Biomedical Data Sets

Exploratory matrix factorization (EMF) techniques applied to two-way or multi-way biomedical data arrays provide new and efficient analysis tools which are currently explored to analyze large scale data sets like gene expression profiles (GEP) measured on microarrays, lipidomic or metabolomic profiles acquired by mass spectrometry (MS) and/or high performance liquid chromatography (HPLC) as well as biomedical images acquired with functional imaging techniques like functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). Exploratory feature extraction techniques like, for example, Principal Component Analysis (PCA), Independent Component Analysis (ICA) or sparse Nonnegative Matrix Factorization (NMF) yield uncorrelated, statistically independent or sparsely encoded and strictly non-negative features which in case of GEPs are called eigenarrays (PCA), expression modes (ICA) or metagenes (NMF). They represent features which characterize the data sets under study and are generally considered indicative of underlying regulatory processes or functional networks and also serve as discriminative features for classification purposes. In the latter case, EMF techniques, when combined with diagnostic a priori knowledge, can directly be applied to the classification of biomedical data sets by grouping samples into different categories for diagnostic purposes or group genes, lipids, metabolic species or activity patches into functional categories for further investigation of related metabolic pathways and regulatory or functional networks. Although these techniques can be applied to large scale data sets in general, the following discussion will primarily focus on applications to microarray data sets and PET images