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See detailA comparative study of automatic classifiers to recognize speakers based on fricatives
Hosseini Kivanani, Nina UL; Asadi, Homa; Schommer, Christoph UL et al

Poster (2022, July)

Speakers’ voices are highly individual and for this reason speakers can be identified based on their voice. Nevertheless, voices are often more variable within the same speaker than they are between ... [more ▼]

Speakers’ voices are highly individual and for this reason speakers can be identified based on their voice. Nevertheless, voices are often more variable within the same speaker than they are between speakers, which makes it difficult for humans and machines to differentiate between speakers (Hansen, J. H., & Hasan, T., 2015). To date, various machine learning methods have been developed to recognize speakers based on the acoustic characteristics of their speech; however, not all of them have proven equally effective in speaker identification, and depending on the obtained techniques, the system achieves a different result. Here, different machine learning classifiers have been applied to identify the best classification model (i.e., Naïve Bayes (NB), support vector machines (SVM), random forests (RF), & k-nearest neighbors (KNN)) for categorizing 4 speaking styles based on the segment types (voiceless fricatives) considering acoustic features of center of gravity, standard deviation, and skewness. We used a dataset consisting of speech samples from 7 native Persian subjects speaking in 4 different speaking styles: read, spontaneous, clear, and child-directed speech. The results revealed that the best performing model to predict the speakers based on the segment type was RF model with an accuracy of 81,3%, followed by SVM (76.3%), NB (75.4%), and KNN (74%) (Table 1). Our results showed that the RF performed the best for voiceless fricatives /f/, /s/, and / ʃ / which may indicate that these segments are much more speaker-specific than others (Gordon et al., 2002), and the model performance was low for the voiceless fricatives of /h/ and /x/. Performance can be seen in the confusion matrix (Figure 1), which produced high precision and recall values (above 80%) for /f/, /s/ and / ʃ / (Table 2). We found that the model performance improved when the data related to clear speaking style; the information in individual speakers (i.e., voiceless fricatives) are more distinguishable in clear style than other styles (Table 1). [less ▲]

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See detailAbout AI and Arts
Schommer, Christoph UL

Speeches/Talks (2021)

From a technical point of view, one can say that there have been extraordinary developments in the broad areas of Artificial Intelligence and Data Science that increasingly determine our lives. Think, for ... [more ▼]

From a technical point of view, one can say that there have been extraordinary developments in the broad areas of Artificial Intelligence and Data Science that increasingly determine our lives. Think, for example, of the many intelligent assistants that are playing an ever greater role in road traffic, in hospitals and in many other areas of application. Art is affected by this as well, because the computer-assisted and AI-supported implementation of creative ideas and thoughts and experimentation with new things have led to innovative results. [less ▲]

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See detailFuture living with AI and IA
Schommer, Christoph UL

Speeches/Talks (2021)

The present and the future will be determined by topics such as artificial intelligence and the question arises to what extent an often quoted usefulness, an "AI for Social Good" and "AI is for Humans ... [more ▼]

The present and the future will be determined by topics such as artificial intelligence and the question arises to what extent an often quoted usefulness, an "AI for Social Good" and "AI is for Humans" really applies. The lecture is intended to provide food for thought. [less ▲]

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See detailThe Future of Living with AI
Schommer, Christoph UL

Speeches/Talks (2021)

This talk, which was part of a 2-hour panel and delivered as a 20-minute presentation without slides, came at the invitation of the Luxembourg Embassy in Brussels, Belgium. The speech is a statement and ... [more ▼]

This talk, which was part of a 2-hour panel and delivered as a 20-minute presentation without slides, came at the invitation of the Luxembourg Embassy in Brussels, Belgium. The speech is a statement and part of the International Symposium "The Future of Living", supported by EUNIC - EU National Institutes of Culture. The event was under the patronage of the Slovenian Presidency of the European Parliament. In the lecture, I briefly presented some selected ideas, observations, and examples on the symposium topic as well as my view of a future living together. [less ▲]

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See detailGetting Creative - AI and Arts
Schommer, Christoph UL

Speeches/Talks (2021)

Keynote Talk "Getting Creative - AI and Arts"; AIFA - Artificial Intelligence and the Future of Arts

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See detailIst die Künstliche Intelligenz für oder gegen die Menschheit?
Schommer, Christoph UL

Speeches/Talks (2021)

La Commission Luxembourgeoise pour la coopération avec l’UNESCO et la Bibliothèque nationale du Luxembourg invitent à leur cycle de conférences dans le cadre des « Rendez-Vous de l’UNESCO ». Le thème ... [more ▼]

La Commission Luxembourgeoise pour la coopération avec l’UNESCO et la Bibliothèque nationale du Luxembourg invitent à leur cycle de conférences dans le cadre des « Rendez-Vous de l’UNESCO ». Le thème central du cycle est cette année : « Mankind and Media. Rethinking the Roles in the Age of Information » Les dernières années ont en outre montré clairement qu’on est fortement dépendant de la transmission des informations vite et performante : on demande toujours plus de données, plus d’informations, plus de connectivité. Mais peut-on conserver une vue d’ensemble dans ce flux d’information hyperrapide ? Peut-on encore contrôler la transformation des informations par les médias numériques ? Où en est l’humain devant l’évolution exponentielle des technologies, de l’intelligence artificielle et du commerce de nos données ? Qui reste à l’origine de l’information et qui comment nous l’interprétons ? Les conférences traitent cette thématique en différentes perspectives. Le 25 février, Prof. Dr. Christoph Schommer de l’Université du Luxembourg fera l’ouverture en posant la question : L’intelligence Artificielle? Où en sommes-nous ? Il abordera les possibilités, chances et risques de ces nouvelles technologies. 4 journalistes se réuniront le 22 avril et discuteront, animés par Josée Hansen, la transmission d’informations et l’objectivité dans le temps des « Fake News » et du « Click Bait ». Dr. Manuela di Franco nous introduira le 1er juillet dans le monde des dessins animés et se penchera sur leur rôle dans la transmission des messages subliminaux et propagandistes. Ian di Toffoli et Prof. Dr. Lukas K. Sosoe s’entretiendront le 30 septembre sur les problèmes et limites de l’éthique dans le monde digital. Qu’il ne faut pas oublier l’art dans la transmission des messages nous rappelleront les 4 artistes qui poursuivront le 2nd décembre, dans la dernière table ronde de ce cycle modérée par Dr. Nora Schleich, le rôle de la liberté artistique face à la libre expression des opinions. Que peut et même doit faire l’art ? La Bibliothèque nationale du Luxembourg aménagera pour chacune de ces soirées, qui auront lieu toujours jeudi à 19hrs, une salle assez grande pour assurer la réunion en présence physique et, le cas échéant, en respect des restrictions actuelles. 25.02.2021 Der Mensch und die Künstliche Intelligenz. Wo stehen wir? (DE) Un entretien avec Prof. Christoph Schommer, modéré par Dr. Nora Schleich, traduit en anglais 22.04.2021 Mediepluralismus – Eng Garantie fir Demokratie am 21. Joerhonnert? (LU) Une table-ronde avec François Aulner, Pia Oppel, Christoph Bumb et Jean-Louis Siweck, modérée par Josée Hansen 01.07.2021 Transmission of Information via Specific Media – Propagandistic Messages in Comics (EN) Une présentation de Dr. Manuela di Franco, traduit en français 30.09.2021 L’Ethique dans la société de l’Information (FR) Un discours avec Prof. Lukas Sosoe, modéré par Ian de Toffoli, traduit en anglais 02.12.2021 Artistesch Fräiheet a Meenungsfräiheet (LU) Une table-ronde avec Justine Blau, Dr. Cédric Kayer, Filip Markiewicz et Anne Simon., modérée par Dr. Nora Schleich Comme les places sont limitées, il faudra s’inscrire au plus tard trois jours avant la conférence en envoyant un courriel à : reservation@bnl.etat.lu Pour plus d’informations, veuillez accéder le site internet : www.unesco.lu; www.bnl.lu [less ▲]

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See detailProceedings of the AI4Health Lecture Series (2021)
Schommer, Christoph UL; Sauter, Thomas UL; Pang, Jun UL et al

Scientific Conference (2021)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [less ▲]

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See detailProceedings of BNAIC/BeneLearn 2021
Leiva, Luis A. UL; Pruski, Cedric UL; Markovich, Réka UL et al

Book published by BnL (2021)

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See detailThe potential of Language Technology and AI
Schommer, Christoph UL

Speeches/Talks (2020)

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Peer Reviewed
See detailBuilding up Explainability in Multi-layer Perceptrons for Credit Risk Modeling
Sharma, Rudrani; Schommer, Christoph UL; Vivarelli, Nicolas

in Sharma, Rudrani (Ed.) Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling (2020, October 09)

Granting loans is one of the major concerns of financial institutions due to the risks of default borrowers. Default prediction by the neural networks is a popular technique for credit risk modeling ... [more ▼]

Granting loans is one of the major concerns of financial institutions due to the risks of default borrowers. Default prediction by the neural networks is a popular technique for credit risk modeling. Neural networks generally offer the accurate predictions that help banks to prevent financial losses and grow their business by approving more creditworthy borrowers. Although neural networks are capable of capturing the complex, non-linear relationships between a large number of features and output, these models act as black boxes. This is a graduation project paper that is focused on loan default risk prediction by multi-layer perceptron neural network and building up explainability to some degree in the trained neural networks through sensitivity analysis. The architecture of a multi-layer perceptron neural network with the best result is used to help the credit-risk manager in explaining why an applicant is a defaulter or non-defaulter. The prediction of a trained multi-layer perceptron neural network is explained by mapping input features and target variables directly using a model-agnostic explanation as well as a modelspecific explanation. Lastly, a comparison is performed between two explanation methods. [less ▲]

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See detailUsing word embeddings to explore the Aboriginality discourse in a corpus of Australian Aboriginal autobiographies
Kamlovskaya, Ekaterina UL; Schommer, Christoph UL

Scientific Conference (2020, September 29)

This submission presents intermediate results of the PhD project analysing discourses in a corpus of Australian Aboriginal autobiographies with word embedding modelling.

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See detailAn Annotation Framework for Luxembourgish Sentiment Analysis
Sirajzade, Joshgun UL; Gierschek, Daniela UL; Schommer, Christoph UL

in Besacier, Laurent; Sakti, Sakriani; Soria, Claudia (Eds.) et al Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) (2020, May)

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and ... [more ▼]

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view. [less ▲]

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See detailComponent Analysis of Adjectives in Luxembourgish for Detecting Sentiments
Sirajzade, Joshgun UL; Gierschek, Daniela UL; Schommer, Christoph UL

in Beermann, Dorothee; Besacier, Laurent; Sakti, Sakriani (Eds.) et al Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop(SLTU-CCURL 2020) (2020, May)

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television ... [more ▼]

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Lëtzebuerg). Alongside many textual features or representations, adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence, and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -bar and the prefix on- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance. [less ▲]

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See detailEine Doktorarbeit zu beginnen, ist (relativ) leicht...
Schommer, Christoph UL

Article for general public (2020)

Vor einigen Wochen war es wieder soweit: Zwei Doktorandinnen hatten ihre wissenschaftlichen Arbeiten eingereicht, sich den Fragen einer internationalen Expertenkommission gestellt, und schließlich ihre ... [more ▼]

Vor einigen Wochen war es wieder soweit: Zwei Doktorandinnen hatten ihre wissenschaftlichen Arbeiten eingereicht, sich den Fragen einer internationalen Expertenkommission gestellt, und schließlich ihre Doktorarbeiten verteidigt. [less ▲]

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See detailZwei Doktorarbeiten zwischen Geist und Informatik
Schommer, Christoph UL

E-print/Working paper (2020)

The article is a snapshot and presents two doctoral students who finished their dissertations at the beginning of 2020: Dr Siwen Guo and Dr Tahereh Pazouki.

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See detailSpeech Based Estimation of Parkinson’s Disease Using Gaussian Processes and Automatic Relevance Determination
Despotovic, Vladimir UL; Skovranek, Tomas; Schommer, Christoph UL

in Neurocomputing (2020), 401

Parkinson’s disease is a progressive neurodegenerative disorder often accompanied by impairment in articulation, phonation, prosody and fluency of speech. In fact, speech impairment is one of the earliest ... [more ▼]

Parkinson’s disease is a progressive neurodegenerative disorder often accompanied by impairment in articulation, phonation, prosody and fluency of speech. In fact, speech impairment is one of the earliest Parkinson’s disease symptoms, and may be used for early diagnosis. We present an experimental study of identification of Parkinson’s disease and assessment of disease progress from speech using Gaussian processes, which is further combined with Automatic Relevance Determination (ARD) for efficient feature selection. Hyperparameters of ARD covariance functions are learned for each individual feature; therefore, can be used for evaluation of their importance. In that way only a small subset of highly relevant acoustic features is selected, leading to models with better performance and lower complexity. The performance of the proposed method was assessed on two datasets: Parkinson’s disease detection dataset, which contains a range of biomedical voice measurements obtained from 31 subjects, 23 of them suffering from Parkinson’s disease and 8 healthy subjects; and Parkinson’s telemonitoring dataset, containing biomedical voice measurements collected from 42 Parkinson’s disease patients for estimation of the disease progress. Gaussian process classification with automatic relevance determination is able to successfully discriminate between Parkinson’s disease patients and healthy controls with 96.92% accuracy, outperforming Support Vector Machines and decision tree ensembles (random forests, boosted and bagged decision trees). The usability of Gaussian processes is further confirmed in regression task for tracking the progress of the disease. [less ▲]

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See detailProceedings of the AI4Health Lecture Series (2020)
Schommer, Christoph UL; Sauter, Thomas UL; Pang, Jun UL et al

Scientific Conference (2020)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [less ▲]

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See detailA Personalized Sentiment Model with Textual and Contextual Information
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019 (2019, November)

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In ... [more ▼]

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information. [less ▲]

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See detailKünstliche Intelligenz für die Medizin
Schommer, Christoph UL

Article for general public (2019)

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and ... [more ▼]

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and healthcare. This article presents some scientific innovations, but at the same time pleads for the inclusion of important factors such as explainability, ethics, comprehensibility and others. [less ▲]

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See detailTopic-based Historical Information Selection for Personalized Sentiment Analysis
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019 (2019, April)

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to ... [more ▼]

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to modeling individuality and understanding the current state of the person. We consider a user's expressions in the past as historical information, and target posts from social platforms for which Twitter texts are chosen as exemplary. While implementing the personalized model PERSEUS, we observed information loss due to the lack of flexibility regarding the design of the input sequence. To compensate this issue, we provide a procedure for information selection based on the similarities in the topics of a user's historical posts. Evaluation is conducted comparing different similarity measures, and improvements are seen with the proposed method. [less ▲]

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