Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

On cumulant techniques in speech processing

This paper analyzes applications of cumulant analysis in speech processing. A special focus is made on different second-order statistics. A dominant role is played by an integral representation for cumulants by means of integrals involving cyclic products of kernels.

Synthesis of Janus compounds for the recognition of G-U mismatched nucleobase pairs

The design and synthesis of two Janus-type heterocycles with the capacity to simultaneously recognize guanine and uracyl in G-U mismatched pairs through complementary hydrogen bond pairing is described. Both compounds were conveniently functionalized with a carboxylic function and efficiently attached to a tripeptide sequence by using solid-phase methodologies. Ligands based on the derivatization of such Janus compounds with a small aminoglycoside, neamine, and its guanidinylated analogue have been synthesized, and their interaction with Tau RNA has been investigated by using several biophysical techniques, including UV-monitored melting curves, fluorescence titration experiments, and 1H NMR. The overall results indicated that Janus-neamine/guanidinoneamine showed some preference for the +3 mutated RNA sequence associated with the development of some tauopathies, although preliminary NMR studies have not confirmed binding to G-U pairs. Moreover, a good correlation has been found between the RNA binding affinity of such Janus-containing ligands and their ability to stabilize this secondary structure upon complexation.

Traditional cockney and popular London speech

This paper gives a full description of the phonetics and phonology of Traditional Cockney and Popular London speech, treating these varieties as constituting a continuum rather than two separate dialects. Exemplification of the vowels, diphthongs and consonants is provided, both in isolate words and in connected speech, along with their range of variation. The frequencies of the vowels have been charted on the basis of the pronunciation of three elderly male speakers. Regarding the consonants, there are detailed observations on the features typically associated with the linguistic varieties examined: strong aspiration of unvoiced plosives, glottalization, H-dropping, L-vocalization and TH-fronting. A section on prosody provides coverage of lexical stress, rhythm and intonation. The paper takes into account up-to-date research on these phenomena, but does not deal with the most recent vowel shifts, some of which form part of Multi-cultural London English.

Recognition of prior learning in online postgraduate education

The recognition of prior experiential learning (RPEL) involves the assessment ofskills and knowledge acquired by an individual through previous experience, which isnot necessarily related to an academic context. RPEL practices are far from generalisedin higher education, and there is a lack of specific guidelines on how to implement RPLprograms in particular settings, such as management education or online programs. TheRPEL pilot program developed in a Spanish virtual university is used throughout thearticle as the basis for further reflection on the design and implementation of RPEL inonline postgraduate education in the business field. The role of competences as a centraltheoretical foundation for RPEL is explained, and the context and characteristics of theRPEL program described. Special attention is paid to the key elements of the program¿sdesign and to the practical aspects of its implementation. The results of the program areassessed and general conclusions and suggestions for further research are discussed.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Towards a lightweight adaptative multicomponential affective recognition system

As part of the Affective Computing research field, the development of automatic affective recognition systems can enhance human-computer interactions by allowing the creation of interfaces that react to the user's emotional state. To that end, this Master Thesis brings affect recognition to nowadays most used human computer interface, mobile devices, by developing a facial expression recognition system able to perform detection under the difficult conditions of viewing angle and illumination that entails the interaction with a mobile device. Moreover, this Master Thesis proposes to combine emotional features detected from expression with contextual information of the current situation, to infer a complex and extensive emotional state of the user. Thus, a cognitive computational model of emotion is defined that provides a multicomponential affective state of the user through the integration of the detected emotional features into appraisal processes. In order to account for individual differences in the emotional experience, these processes can be adapted to the culture and personality of the user.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Melodic Analysis of Speech Method applied to Spanish and Catalan

In this paper, we present the Melodic Analysis of Speech method (MAS) that enables us to carry out complete and objective descriptions of a language's intonation, from a phonetic (melodic) point of view as well as from a phonological point of view. It is based on the acoustic-perceptive method by Cantero (2002), which has already been used in research on prosody in different languages. In this case, we present the results of its application in Spanish and Catalan.