Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect of substantial applications, notably in human-computer interaction. Progress in the area relies heavily on the development of appropriate databases. This paper addresses four main issues that need to be considered in developing databases of emotional speech: scope, naturalness, context and descriptors. The state of the art is reviewed. A good deal has been done to address the key issues, but there is still a long way to go. The paper shows how the challenge of developing appropriate databases is being addressed in three major recent projects--the Reading-Leeds project, the Belfast project and the CREST-ESP project. From these and other studies the paper draws together the tools and methods that have been developed, addresses the problems that arise and indicates the future directions for the development of emotional speech databases. Ó 2002 Elsevier Science B.V. All rights reserved.Re esume e LÕe etude de la parole et de lÕe emotion, partie du stade de la recherche exploratrice, en arrive maintenant au stade qui est celui dÕapplications importantes, notamment dans lÕinteraction homme-machine. Le progre es en ce domaine de epend e etroitment du de eveloppement de bases de donne ees approprie ees. Cet article aborde quatre points principaux qui me eritent notre attention a a ce sujet: lÕe etendue, lÕauthenticite e, le contexte et les termes de description. Il pre esente un compte-rendu de la situation actuelle dans ce domaine et e evoque les avance ees faites, et celles qui restent a a faire. LÕarticle montre comment trois re ecents projets importants (celui de Reading-Leeds, celui de Belfast, et celui de CREST-ESP) ont releve e le de efi pose e par la construction de bases de donne ees approprie ees. A partir de ces trois projets, ainsi que dÕautres travaux, les auteurs pre esentment un bilan des outils et me ethodes utilise es, identifient les proble emes qui y sont associe es, et indiquent la direction dans laquelle devraient sÕorienter les recherches a a venir.

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The complete arrangement of genes in the mitochondrial (mt) genome is known for 12 species of insects, and part of the gene arrangement in the mt genome is known for over 300 other species of insects. The arrangement of genes in the mt genome is very conserved in insects studied, since all of the protein-coding and rRNA genes and most of the tRNA genes are arranged in the same way. We sequenced the entire mt genome of the wallaby louse, Heterodoxus macropus, which is 14,670 bp long and has the 37 genes typical of animals and some noncoding regions. The largest noncoding region is 73 bp long (93% A+T), and the second largest is 47 bp long (92% A+T). Both of these noncoding regions seem to be able to form stem-loop structures. The arrangement of genes in the mt genome of this louse is unlike that of any other animal studied. All tRNA genes have moved and/or inverted relative to the ancestral gene arrangement of insects, which is present in the fruit fly Drosophila yakuba. At least nine protein-coding genes (atp6, atp8, cox2, cob, nad1-nad3, nad5, and nad6) have moved; moreover, four of these genes (atp6, atp8, nad1, and nad3) have inverted. The large number of gene rearrangements in the mt genome of H. macropus is unprecedented for an arthropod.

We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.

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