Romance languages

The Romance languages, sometimes referred to as Latin languages or Neo-Latin languages, are numerous modern languages that evolved from Late Latin and its spoken form, often called Vulgar Latin. They are the only extant subgroup of the Italic languages branch of the Indo-European language family. The five most widely spoken Romance languages by number of native speakers are Spanish (489 million), Portuguese (283 million), French (80 million), Italian (67 million) and Romanian (24 million), which are all national languages of their respective countries of origin. There are more than 900 million native speakers of Romance languages found worldwide, mainly in the Americas, Europe, and parts of Africa. The major Romance languages also have many non-native speakers and are in widespread use as linguae francae. Name and languages The term Romance derives from the Vulgar Latin adverb romanice, "in Roman", derived from romanicus: for instance, in the expression romanice loqui, "t

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This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.

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Multilingual bottleneck features for subword modeling in zero-resource languages

Enno Hermann

How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sense to leverage information from large annotated corpora in other languages, for example in the form of multilingual bottleneck features (BNFs) obtained from a supervised speech recognition system. In this work, we evaluate the benefits of BNFs for subword modeling (feature extraction) in six unseen languages on a word discrimination task. First we establish a strong unsupervised baseline by combining two existing methods: vocal tract length normalisation (VTLN) and the correspondence autoencoder (cAE). We then show that BNFs trained on a single language already beat this baseline; including up to 10 languages results in additional improvements which cannot be matched by just adding more data from a single language. Finally, we show that the cAE can improve further on the BNFs if high-quality same-word pairs are available.

2018

Intégration. Réfugiés dans quartier résidentiel

Katerina Skarkova

Affluant en l'Europe, le nombre de réfugiés des pays affectés par des conflits politiques ne cesse d'augmenter. La question de l'intégration de ces migrants est au coeur des discussions politiques, et pourtant les réponses proposées ne sont toujours pas satisfaisantes. En effet, l'hébergement des réfugiés en périphérie, dans des baraques ou des casernes abandonnées – la solution habituellement prisée – n'aide pas à l'intégration et la rend, au contraire, d'autant plus difficile, voire impossible. Quelle serait la situation si les migrants pouvaient partager les quartiers résidentiels des habitants locaux? Cela baisserait-il la vague de mécontentement des locaux envers les nouveaux arrivés? Comment fonctionnerait une telle coexistance dans les quartiers centraux de nos villes? Cette idée est imaginée dans le quartier bernois de la “Lorraine“. Une cour, bordée de fonctions publiques et communautaires pouvant être utilisées par les migrants et les locaux, crée un nouvel espace public dans la zone bâtie existante. La cour permet des échanges tant au niveau matériel que social: un échange de travail, de marchandise, de connaissance et de culture. Aux étages supérieurs, les colocations cohabitées par les migrants et les résidents locaux permettent cette fois un échange dans un environnement plus privatif. Le projet offre aux réfugiés la possibilité de s'intégrer activement et de manière constructive à la vie quotidienne de la nouvelle culture, et permet parallèlement aux habitants du quartier de profiter des savoir-faire des nouveaux venus.

2015

Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Enno Hermann

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this area has primarily focused unsupervised learning from target language data only, and has been evaluated only intrinsically. Here we directly compare multiple methods, including some that use only target language speech data and some that use transcribed speech from other (non-target) languages, and we evaluate using two intrinsic measures as well as on a downstream unsupervised word segmentation and clustering task. We find that combining two existing target-language-only methods yields better features than either method alone. Nevertheless, even better results are obtained by extracting target language bottleneck features using a model trained on other languages. Cross-lingual training using just one other language is enough to provide this benefit, but multilingual training helps even more. In addition to these results, which hold across both intrinsic measures and the extrinsic task, we discuss the qualitative differences between the different types of learned features.

2020

French language

French (Standard French: Français fʁɑ̃sɛ or langue française lɑ̃ɡ fʁɑ̃sɛz) is a Romance language of the Indo-European family. It descended from the Vulgar Latin of the Roman Empire, as did all Romance

Latin

Latin (lingua Latīna ˈlɪŋɡʷa ɫaˈtiːna or Latīnum ɫaˈtiːnʊ̃) is a classical language belonging to the Italic branch of the Indo-European languages. Latin was originally a dialect spoken in Latium (also

Italian language

Italian (italiano itaˈljaːno or lingua italiana ˈliŋɡwa itaˈljaːna) is a Romance language of the Indo-European language family that evolved from the Vulgar Latin of the Roman Empire. Together with Sar