Acoustic Word Embeddings for Zero-Resource Languages Using Self-Supervised Contrastive Learning and Multilingual Adaptation

Acoustic word embeddings (AWEs) are fixed-dimensional representations of variable-length speech segments. For zero-resource languages where labelled data is not available, one AWE approach is to use unsupervised autoencoder-based recurrent models. Another recent approach is to use multilingual transfer: a supervised AWE model is trained on several well-resourced languages and then applied to an unseen zero-resource language. We consider how a recent contrastive learning loss can be used in both the purely unsupervised and multilingual transfer settings. Firstly, we show that terms from an unsupervised term discovery system can be used for contrastive self-supervision, resulting in improvements over previous unsupervised monolingual AWE models. Secondly, we consider how multilingual AWE models can be adapted to a specific zero-resource language using discovered terms. We find that self-supervised contrastive adaptation outperforms adapted multilingual correspondence autoencoder and Siamese AWE models, giving the best overall results in a word discrimination task on six zero-resource languages.Comment: Accepted to SLT 202

Pharmaceutical composition to protect an animal against a disorder arising from an infection with a bacterium that belongs to the group of nocardioform actinomycetes

The invention pertains to a pharmaceutical composition to protect an animal against a disorder arising from an infection with a bacterium that belongs to the group of nocardioform actinomycetes having the ability to survive within macrophages of the animal, comprising live bacteria of a nocardioform actinomycetes species, the live bacteria being attenuated by inactivation of a gene that encodes a protein involved in methylhexahydroindanedione propionate degradation, and a pharmaceutically acceptable carrier for these live bacteria.<br/

Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili

We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource language. We compare this to using acoustic word embedding (AWE) models that map speech segments to a space where matching words have similar vectors. We specifically use a multilingual AWE model trained on labelled data from well-resourced languages to spot keywords in data in the unseen target language. In contrast to ASR, the AWE approach only requires a few keyword exemplars. In controlled experiments on Wolof and Swahili where training and test data are from the same domain, an ASR model trained on just five minutes of data outperforms the AWE approach. But in an in-the-wild test on Swahili radio broadcasts with actual hate speech keywords, the AWE model (using one minute of template data) is more robust, giving similar performance to an ASR system trained on 30 hours of labelled data.Comment: Accepted to Interspeech 202

An analysis of water consumption in Europe’s energy production sector: The potential impact of the EU Energy Reference Scenario 2013 (LUISA configuration 2014)

This report presents the outcome of a study carried out in the frame of a wider assessment performed with the LUISA (Land Use-based Integrated Sustainability Assessment) modelling platform, configured in compliance with the “EU Energy, Transport and GHG emissions trends until 2050” (EU Energy Reference Scenario 2013). A new methodology has been implemented to estimate and map water requirements for energy production in Europe. In this study, the category of dedicated energy crops (ENCR) played an important role. These crops are expected to emerge as additional fuel sources within the EU28 by 2020. Water requirements in the remaining energy sectors have also been estimated in order to assess whether the introduction of these ENCR may, in any way, compete with the existing water requirements for energy production. More specifically, the study tackles the following questions: • Where and to what extent will there be potential competition with cooling water required for electricity generation related to the introduction of these crops? • How will these trends evolve over time? • How will the introduction of energy crops affect the overall water consumption trends in Europe? The analysis indicates that high irrigation requirements for ENCR are foreseen in France, Poland, Spain, eastern Germany, and regions of Italy and the UK. Substantial increases in requirements are seen for several regions from 2020 to 2030. ENCR are absent in Finland, Denmark, Greece, Malta, Cyprus and Croatia for the whole simulation period. Water consumption for cooling in electricity production has been quantified for the years 2020 and 2030 for 2 scenarios with a minimum and a maximum value. There is notable variation in overall water consumption, both over time and between the scenarios. There is an increase in cooling water consumption for most regions in both scenarios over the period 2020 to 2030, which is especially high in France for the minimum scenario. The values given by the two scenarios vary greatly due to the wide range in water consumption between the different cooling technologies assumed in the two cases. In some regions there is even up to a factor 10 difference in total consumption for cooling. As for any modelling exercise, the study presents a level of uncertainty due to the number of external models giving input and to the assumptions made. In the case of the cooling water mapping, a possible range of minimum/maximum values has been used to reflect the large variation due to the type of cooling system used by each power plant. For the energy crop water requirements we relied on estimates found in the literature. Nevertheless, the study presents an overall continental scale analysis of the potential impacts of the 2013 Energy Reference scenario, covering many of the involved sectors and provides the framework for further refinements and improvements.JRC.B.3-Territorial Developmen

Impact of a changing climate, land use, and water usage on water resources in the Danube river basin

Impact of a changing climate, land use, and water usage on water resources in the Danube river basinJRC.D.2-Water and Marine Resource

Accessibility and territorial cohesion in a case of transport infrastructure improvements with changing population distributions

In the last decade or so many studies have looked into the impacts of infrastructure improvements on decreasing territorial disparities. In those studies population levels are usually assumed static, although future population levels likely change in response to changing accessibility levels as well as to other factors. This study uses future population distributions simulated by the LUMP land-use model to assess the impacts of large transport network investments on regional accessibility disparities. The results indicate that contrasting local urbanization patterns only modestly affect average national accessibility levels, but that those patterns have a considerable effect on regional inequality indicators. This underpins the importance of incorporating future population levels when assessing cohesion impacts of infrastructure investments.JRC.H.8-Sustainability Assessmen