Multilingual Adaptation of RNN Based ASR Systems

In this work, we focus on multilingual systems based on recurrent neural networks (RNNs), trained using the Connectionist Temporal Classification (CTC) loss function. Using a multilingual set of acoustic units poses difficulties. To address this issue, we proposed Language Feature Vectors (LFVs) to train language adaptive multilingual systems. Language adaptation, in contrast to speaker adaptation, needs to be applied not only on the feature level, but also to deeper layers of the network. In this work, we therefore extended our previous approach by introducing a novel technique which we call "modulation". Based on this method, we modulated the hidden layers of RNNs using LFVs. We evaluated this approach in both full and low resource conditions, as well as for grapheme and phone based systems. Lower error rates throughout the different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Evolution of cooperation in artificial ants

The evolution of cooperation is a fundamental and enduring puzzle in biology and the social sciences. Hundreds of theoretical models have been proposed, but empirical research has been hindered by the generation time of social organisms and by the difficulties of quantifying costs and benefits of cooperation. The significant increase in computational power in the last decade has made artificial evolution of simple social robots a promising alternative. This thesis is concerned with the artificial evolution of groups of cooperating robots. It argues that artificial evolution of robotic agents is a powerful tool to address open questions in evolutionary biology, and shows how insights gained from the study of artificial and biological multi-agent systems can be mutually beneficial for both biology and robotics. The work presented in this thesis contributes to biology by showing how artificial evolution can be used to quantify key factors in the evolution of cooperation in biological systems and by providing an empirical test of a central part of biological theory. In addition, it reveals the importance of the genetic architecture for the evolution of efficient cooperation in groups of organisms. The work also contributes to robotics by identifying three different classes of multi-robot tasks depending on the amount of cooperation required between team members and by suggesting guidelines for the evolution of efficient robot teams. Furthermore it shows how simulations can be used to successfully evolve controllers for physical robot teams

Towards Improving Low-Resource Speech Recognition Using Articulatory and Language Features

In an increasingly globalized world, there is a rising demand for speech recognition systems. Systems for languages like English, German or French do achieve a decent performance, but there exists a long tail of languages for which such systems do not yet exist. State-of-the-art speech recognition systems feature Deep Neural Networks (DNNs). Being a data driven method and therefore highly dependent on sufficient training data, the lack of resources directly affects the recognition performance. There exist multiple techniques to deal with such resource constraint conditions, one approach is the use of additional data from other languages. In the past, is was demonstrated that multilingually trained systems benefit from adding language feature vectors (LFVs) to the input features, similar to i-Vectors. In this work, we extend this approach by the addition of articulatory features (AFs). We show that AFs also benefit from LFVs and that multilingual system setups benefit from adding both AFs and LFVs. Pretending English to be a low-resource language, we restricted ourselves to use only 10h of English acoustic training data. For system training, we use additional data from French, German and Turkish. By using a combination of AFs and LFVs, we were able to decrease the WER from 18.1% to 17.3% after system combination in our setup using a multilingual phone set

Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment

Evaluation of the KIT Lecture Translation System

To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next

Measures of Diversity for Populations and Distances Between Individuals with Highly Reorganizable Genomes

In this paper we address the problem of defining a measure of diversity for a population of individuals whose genome can be subjected to major reorganizations during the evolutionary process. To this end, we introduce a measure of diversity for populations of strings of variable length defined on a finite alphabet, and from this measure we derive a semi-metric distance between pairs of strings. The definitions are based on counting the number of substrings of the strings, considered first separately and then collectively. This approach is related to the concept of linguistic complexity, whose definition we generalize from single strings to populations. Using the substring count approach we also define a new kind of Tanimoto distance between strings. We show how to extend the approach to representations that are not based on strings and, in particular, to the tree-based representations used in the field of genetic programming. We describe how suffix trees can allow these measures and distances to be implemented with a computational cost that is linear in both space and time relative to the length of the strings and the size of the population. The definitions were devised to assess the diversity of populations having genomes of variable length and variable structure during evolutionary computation runs, but applications in quantitative genomics, proteomics, and pattern recognition can be also envisaged