This paper addresses the issue of automatic emotion recognition in speech. We focus on a type of emotional manifestation which has been rarely studied in speech processing: fear-type emotions occurring during abnormal situations (here, unplanned events where human life is threatened). This study is dedicated to a new application in emotion recognition -public safety. The starting point of this work is the definition and the collection of data illustrating extreme emotional manifestations in threatening situations. For this purpose we develop the SAFE corpus (situation analysis in a fictional and emotional corpus) based on fiction movies. It consists of 7 h of recordings organized into 400 audiovisual sequences. The corpus contains recordings of both normal and abnormal situations and provides a large scope of contexts and therefore a large scope of emotional manifestations. In this way, not only it addresses the issue of the lack of corpora illustrating strong emotions, but also it forms an interesting support to study a high variety of emotional manifestations. We define a task-dependent annotation strategy which has the particularity to describe simultaneously the emotion and the situation evolution in context. The emotion recognition system is based on these data and must handle a large scope of unknown speakers and situations in noisy sound environments. It consists of a fear vs. neutral classification. The novelty of our approach relies on dissociated acoustic models of the voiced and unvoiced contents of speech. The two are then merged at the decision step of the classification system. The results are quite promising given the complexity and the diversity of the data: the error rate is about 30%.

No abstract

No abstract

Detecting emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogs, enabling dynamic modification of the dialog strategies and influencing the final outcome. The emotion detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogs in which the manifestation of emotion is quite complex, and it is common to have shaded emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate emotions for call center services, to annotate the dialogs and to validate the presence of emotions via perceptual tests and to find robust cues for emotion detection. In contrast to research carried out with artificial data with simulated emotions, for real-life corpora the set of appropriate emotion labels must be determined. Two studies are reported: the first investigates automatic emotion detection using linguistic information, whereas the second concerns perceptual tests for identifying emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.

No abstract

No abstract

In this work a novel sensor array platform based on a dual carbon screen-printed electrode was developed for the simultaneous determination of chlorogenic acid and caffeine. One of the carbon working electrodes was modified with platinum nanoparticles, reduced graphene oxide and laccase (C-SPE/Pt-NPs/RGO/lacc-biosensor) for chlorogenic acid determination and the second carbon working electrodes was modified with reduced graphene oxide and Nafion (C-SPE/RGO/Nafion-sensor) for caffeine determination. Cyclic voltammetry was used to characterise and optimise the dual sensor array while chronoamperometry was used to investigate the bioelectrocatalytic response. The C-SPE/Pt-NPs/RGO/lacc for biosensing chlorogenic acid exhibited a sensitivity of 0.02 µA/µM and a detection limit of 2.67 µM whereas the C-SPE/RGO/Nafion used for sensing caffeine has showed a sensitivity of 1.38 µA/µM and a detection limit of 0.22 µM. The developed sensor array was used to determine these two major coffee beans compounds from real coffee samples. Due to its simplicity, feasibility and accessibility, the developed dual sensor array could represent the basis of a valuable analytical tool able to screen both chlorogenic acid and caffeine content from coffee samples offering important information about the phytochemical composition of the samples.

The present work describes the development of a nanocomposite system and its application in construction of a new amperometric biosensor applied in the determination of total polyphenolic content from propolis extracts. The nanocomposite system was based on covalent immobilization of laccase on functionalized indium tin oxide nanoparticles and it was morphologically and structural characterized. The casting of the developed nanocomposite system on the surface of a screen-printed electrode was used for biosensor fabrication. The analytical performance characteristics of the settled biosensor were determined for rosmarinic acid, caffeic acid and catechol (as laccase specific substrate). The linearity was obtained in the range of 1.06×10−6 − 1.50×10−5 mol L−1 for rosmarinic acid, 1.90×10−7 − 2.80×10−6 mol L−1 for caffeic acid and 1.66×10−6 − 7.00×10−6 mol L−1 for catechol. A good sensitivity of amperometric biosensor 141.15 nA µmol−1 L−1 and fair detection limit 7.08×10−8 mol L−1 were obtained for caffeic acid. The results obtained for polyphenolic content of propolis extracts were compared with the chromatographic data obtained by liquid-chromatography with diode array detection.