Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Linguistic biomarkers for the detection of Mild Cognitive Impairment

A timely diagnosis of the prodromal stages of dementia remains a big challenge for healthcare systems: many assessment tools have been proposed over recent years, but the commonest screening instruments are largely unreliable for detecting subtle changes in cognition. The scientific literature contains a rising number of reports about language disturbances at the earliest stages of dementia, a clinical syndrome known as “Mild Cognitive Impairment" (MCI). Here we take advantage of these findings to develop a novel NLP method capable of identifying cognitive frailty at a very early stage by processing Italian spoken productions. This study constitutes a first step in the creation of an automatic tool for non-intrusive, low-cost dementia screening exploiting linguistic biomarkers. Our findings show that acoustic features (i.e., fluency indexes and spectral properties of the voice) are the most reliable parameters for MCI early identification. Moreover, lexical and syntactic features, grabbing the erosion of verbal abilities caused by the pathology, emerge as statistically significant and can support speech traits in the classification process

Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

ABSTRACT: In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wavelet based features are used to characterize emotions in different databases created for such purpose

A review of Yorùbá Automatic Speech Recognition

Automatic Speech Recognition (ASR) has recorded appreciable progress both in technology and application.Despite this progress, there still exist wide performance gap between human speech recognition (HSR) and ASR which has inhibited its full adoption in real life situation.A brief review of research progress on Yorùbá Automatic Speech Recognition (ASR) is presented in this paper focusing of variability as factor contributing to performance gap between HSR and ASR with a view of x-raying the advances recorded, major obstacles, and chart a way forward for development of ASR for Yorùbá that is comparable to those of other tone languages and of developed nations.This is done through extensive surveys of literatures on ASR with focus on Yorùbá.Though appreciable progress has been recorded in advancement of ASR in the developed world, reverse is the case for most of the developing nations especially those of Africa.Yorùbá like most of languages in Africa lacks both human and materials resources needed for the development of functional ASR system much less taking advantage of its potentials benefits. Results reveal that attaining an ultimate goal of ASR performance comparable to human level requires deep understanding of variability factors

The emergence of language as a function of brain-hemispheric feedback

This text posits the emergence of language as a function of brain-hemispheric feedback, where “emergence” refers to the generation of complex patterns from relatively simple interactions, “language” refers to an abstraction-based and representational-recombinatorial-recursive mapping-signaling system, “function” refers to an input-output relationship described by fractal algorithms, “brain-hemispheric” refers to complementary (approach-abstraction / avoidance-gestalt) cognitive modules, and “feedback” refers to self-regulation driven by neural inhibition and recruitment. The origin of language marks the dawn of human self-awareness and culture, and is thus a matter of fundamental and cross-disciplinary interest. This text is a synthesized research essay that constructs its argument by drawing diverse scholarly voices into a critical, cross-disciplinary intertextual narrative. While it does not report any original empirical findings, it harnesses those made by others to offer a tentative, partial solution—one that can later be altered and expanded—to a problem that has occupied thinkers for centuries. The research contained within this text is preceded by an introductory Section 1 that contextualizes the problem of the origin of language. Section 2 details the potential of evolutionary theory for addressing the problem, and the reasons for the century-long failure of linguistics to take advantage of that potential. Section 3 reviews the history of the discovery of brain lateralization, as well as its behavioral and structural characteristics. Section 4 discusses evolutionary evidence and mechanisms in terms of increasing adaptive complexity and intelligence, in general, and tool use, in particular. Section 5 combines chaos theory, brain science, and semiotics to propose that, after the neotenic acquisition of contingency-based abstraction, language emerged as a feedback interaction between the left-hemisphere abstract word and the right-hemisphere gestalt image. I conclude that the model proposed here might be a valuable tool for understanding, organizing, and relating data and ideas concerning human evolution, language, culture, and psychology. I recommend, of course, that I present this text to the scholarly community for criticism, and that I continue to gather and collate relevant data and ideas, in order to prepare its next iteration

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Analysis of the structure of time-frequency information in electromagnetic brain signals

This thesis encompasses methodological developments and experimental work aimed at revealing information contained in time, frequency, and time–frequency representations of electromagnetic, specifically magnetoencephalographic, brain signals. The work can be divided into six endeavors. First, it was shown that sound slopes increasing in intensity from undetectable to audible elicit event-related responses (ERRs) that predict behavioral sound detection. This provides an opportunity to use non-invasive brain measures in hearing assessment. Second, the actively debated generation mechanism of ERRs was examined using novel analysis techniques, which showed that auditory stimulation did not result in phase reorganization of ongoing neural oscillations, and that processes additive to the oscillations accounted for the generation of ERRs. Third, the prerequisites for the use of continuous wavelet transform in the interrogation of event-related brain processes were established. Subsequently, it was found that auditory stimulation resulted in an intermittent dampening of ongoing oscillations. Fourth, information on the time–frequency structure of ERRs was used to reveal that, depending on measurement condition, amplitude differences in averaged ERRs were due to changes in temporal alignment or in amplitudes of the single-trial ERRs. Fifth, a method that exploits mutual information of spectral estimates obtained with several window lengths was introduced. It allows the removal of frequency-dependent noise slopes and the accentuation of spectral peaks. Finally, a two-dimensional statistical data representation was developed, wherein all frequency components of a signal are made directly comparable according to spectral distribution of their envelope modulations by using the fractal property of the wavelet transform. This representation reveals noise buried processes and describes their envelope behavior. These examinations provide for two general conjectures. The stability of structures, or the level of stationarity, in a signal determines the appropriate analysis method and can be used as a measure to reveal processes that may not be observable with other available analysis approaches. The results also indicate that transient neural activity, reflected in ERRs, is a viable means of representing information in the human brain.reviewe

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis