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Signs and Synonymity Continuing Development of the Multilingual Sign language Wordnet 11

Signs and Synonymity Continuing Development of the Multiling...

11th Workshop on the Representation and processing of Sign languages: Evaluation of Sign language Resources, sign-lang@LREC-COLING 2024

作者： Schulder, Marc Bigeard, Sam Kopf, Maria Hanke, Thomas Kuder, Anna Wójcicka, Joanna Mesch, Johanna Björkstrand, Thomas Vacalopoulou, Anna Vasilaki, Kyriaki Goulas, Theodore Fotinea, Stavroula-Evita Efthimiou, Eleni Institute of German Sign Language and Communication of the Deaf University of Hamburg Germany Inria Centre University of Lorraine France Department of Linguistics University of Cologne Germany Department of General Linguistics Sign Language Linguistics and Baltic Studies University of Warsaw Poland Department of Linguistics Stockholm University Sweden Institute for Language and Speech Processing Athena Research Center Greece

ISBN: (纸本)9782493814302

The Multilingual Sign language Wordnet is the first publicly available wordnet resource for sign languages. It is a growing multilingual resource providing data for eight sign languages to date. During the initial phase of its creation, the focus lay on producing the infrastructure to support various languages and to produce initial sets of content for them. This article represents the start of the second phase, in which the focus is moved to establishing overlapping coverage across the different sign languages. Building on the data produced so far, a new feature to assist annotation is introduced which leverages established partial synonymy between signs (inter- and cross-lingually) to discover likely additional synonymies. Other improvements to the annotation interface and workflow build directly on the experiences from the first phase. Working with the updated annotation interface, new data is produced for Polish Sign language, Greek Sign language and Swedish Sign language. © 2024 ELRA language Resources Association: CC BY-NC 4.0.

关键词： Ontology

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WEAKLY-SUPERVISED AUTOMATED AUDIO CAPTIONING VIA TEXT ONLY TRAINING

arXiv

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arXiv 2023年

作者： Kouzelis, Theodoros Katsouros, Vassilis Institute for Language and Speech Processing Athena Research Center Marousi15125 Greece

In recent years, datasets of paired audio and captions have enabled remarkable success in automatically generating descriptions for audio clips, namely Automated Audio Captioning (AAC). However, it is labor-intensive and time-consuming to collect a sufficient number of paired audio and captions. Motivated by the recent advances in Contrastive language-Audio Pretraining (CLAP), we propose a weakly-supervised approach to train an AAC model assuming only text data and a pre-trained CLAP model, alleviating the need for paired target data. Our approach leverages the similarity between audio and text embeddings in CLAP. During training, we learn to reconstruct the text from the CLAP text embedding, and during inference, we decode using the audio embeddings. To mitigate the modality gap between the audio and text embeddings we employ strategies to bridge the gap during training and inference stages. We evaluate our proposed method on Clotho and AudioCaps datasets demonstrating its ability to achieve a relative performance of up to 83% compared to fully supervised approaches trained with paired target data. Our code is available at: https://***/zelaki/wsac. © 2023, CC BY.

关键词： Embeddings

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Designing and Evaluating speech Emotion Recognition Systems: A Reality Check Case Study with IEMOCAP 48

Designing and Evaluating Speech Emotion Recognition Systems:...

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48th IEEE International Conference on Acoustics, speech and Signal processing, ICASSP 2023

作者： Antoniou, Nikolaos Katsamanis, Athanasios Giannakopoulos, Theodoros Narayanan, Shrikanth Behavioral Signal Technologies Los AngelesCA United States Athena Research Center Institute for Language and Speech Processing Athens Greece SAIL-University of Southern California Los AngelesCA United States

ISBN: (纸本)9781728163277

There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test models for SER, published work reveals a wide range of assumptions and variety in its use that challenge reproducibility and generalization. Based on a critical review of the latest advances in SER using IEMOCAP as the use case, our work aims at two contributions: First, using an analysis of the recent literature, including assumptions made and metrics used therein, we provide a set of SER evaluation guidelines. Second, using recent publications with open-sourced implementations, we focus on reproducibility assessment in SER. © 2023 IEEE.

关键词： Emotion Recognition

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LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights

arXiv

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arXiv 2024年

作者： Chlapanis, Odysseas S. Galanis, Dimitrios Androutsopoulos, Ion Department of Informatics Athens University of Economics and Business Greece Institute for Language and Speech Processing Athena Research Center Greece Archimedes Unit Athena Research Center Greece

We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal reasoning capabilities of Large language Models (LLMs). The task requires selecting the correct next statement (from multiple choice options) in a chain of legal arguments from court proceedings, given the facts of the case. We constructed a dataset (LAR-ECHR) for this task using cases from the European Court of Human Rights (ECHR). We evaluated seven general-purpose LLMs on LAR-ECHR and found that (a) the ranking of the models is aligned with that of LegalBench, an established US-based legal reasoning benchmark, even though LAR-ECHR is based on EU law, (b) LAR-ECHR distinguishes top models more clearly, compared to LegalBench, (c) even the best model (GPT-4o) obtains 75.8% accuracy on LAR-ECHR, indicating significant potential for further model improvement. The process followed to construct LAR-ECHR can be replicated with cases from other legal systems. © 2024, CC BY.

关键词： Case based reasoning

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Investigating Personalization Methods in Text to Music Generation

Investigating Personalization Methods in Text to Music Gener...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Manos Plitsis Theodoros Kouzelis Georgios Paraskevopoulos Vassilis Katsouros Yannis Panagakis Institute for Language and Speech Processing Athena Research Center Greece Department of Informatics and Telecommunications University of Athens Greece Archimedes Unit Athena Research Center Greece

In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the first to explore the combination of pre-trained text-to-audio diffusers with two established personalization methods. We experiment with the effect of audio-specific data augmentation on the overall system performance and assess different training strategies. For evaluation, we construct a novel dataset with prompts and music clips. We consider both embedding-based and music-specific metrics for quantitative evaluation, as well as a user study for qualitative evaluation. Our analysis shows that similarity metrics are in accordance with user preferences and that current personalization approaches tend to learn rhythmic music concepts more easily than melody. The code, dataset, and example material of this study are open to the research community 1 .

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HFabD+M: A Web-based Platform for Automated Hyperledger Fabric Deployment and Management 1

HFabD+M: A Web-based Platform for Automated Hyperledger Fabr...

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1st IEEE Global Emerging Technology Blockchain Forum: Blockchain and Beyond, iGETblockchain 2022

作者： Zikos, Ioannis Sendros, Andreas Drosatos, George Efraimidis, Pavlos S. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Athena Research Center Institute for Language and Speech Processing Xanthi67100 Greece

ISBN: (纸本)9781665451987

Hyperledger Fabric is an open-source private permissioned blockchain that supports the use of smart contracts (chaincode). It is aimed mainly at private networks of companies. To serve the different needs of each company and to be flexible in customer requirements, it consists of various adaptive components. Although this structure efficiently addresses a wide range of needs, deploying such a network for research purposes or rapid development is complex. In this paper, we present a web-based system architecture for the automated deployment of a Hyperledger Fabric network, and in addition, we describe the tools needed to manage and update such a network. Finally, as a proof-of-concept, we implement the proposed architecture to demonstrate the feasibility of our approach. © 2022 IEEE.

关键词： Blockchain

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Archimedes-AUEB at SemEval-2024 Task 5: LLM explains Civil Procedure

arXiv

引用

arXiv 2024年

作者： Chlapanis, Odysseas S. Androutsopoulos, Ion Galanis, Dimitrios Department of Informatics Athens University of Economics and Business Greece Institute for Language and Speech Processing Athena Research Center Greece Archimedes Unit Athena Research Center Greece

The SemEval task on Argument Reasoning in Civil Procedure is challenging in that it requires understanding legal concepts and inferring complex arguments. Currently, most Large language Models (LLM) excelling in the legal realm are principally purposed for classification tasks, hence their reasoning rationale is subject to contention. The approach we advocate involves using a powerful teacher-LLM (ChatGPT) to extend the training dataset with explanations and generate synthetic data. The resulting data are then leveraged to fine-tune a small student-LLM. Contrary to previous work, our explanations are not directly derived from the teacher’s internal knowledge. Instead they are grounded in authentic human analyses, therefore delivering a superior reasoning signal. Additionally, a new ‘mutation’ method generates artificial data instances inspired from existing ones. We are publicly releasing the explanations as an extension to the original dataset, along with the synthetic dataset and the prompts that were used to generate both. Our system ranked 15th in the SemEval competition. It outperforms its own teacher and can produce explanations aligned with the original human analyses, as verified by legal experts. © 2024, CC BY.

关键词： Personnel training

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SPECTRE: Visual speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

引用

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Filntisis, Panagiotis P. Retsinas, George Paraperas-Papantoniou, Foivos Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros Athena Research Center Institute of Robotics Maroussi15125 Greece National Technical University of Athens School of Electrical & Computer Engineering Greece Greece University of Exeter College of Engineering Mathematics and Physical Sciences United Kingdom Imperial College London United Kingdom Institute for Language and Speech Processing Athena R.C. Greece

ISBN: (纸本)9798350302493

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading"loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/ © 2023 IEEE.

关键词： Human computer interaction

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Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis

arXiv

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arXiv 2024年

作者： Kouzelis, Theodoros Plitsis, Manos Nicolaou, Mihalis A. Panagakis, Yannis National Technical University of Athens Athens Greece Archimedes AI Athena RC Athens Greece Institute for Language and Speech Athena RC Processing Athens Greece Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus

Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial Networks (GANs). However, the latent space of DMs is not as well understood as that of GANs. Recent research has focused on unsupervised semantic discovery in the latent space of DMs by leveraging the bottleneck layer of the denoising network, which has been shown to exhibit properties of a semantic latent space. However, these approaches are limited to discovering global attributes. In this paper we address, the challenge of local image manipulation in DMs and introduce an unsupervised method to factorize the latent semantics learned by the denoising network of pre-trained DMs. Given an arbitrary image and defined regions of interest, we utilize the Jacobian of the denoising network to establish a relation between the regions of interest and their corresponding subspaces in the latent space. Furthermore, we disentangle the joint and individual components of these subspaces to identify latent directions that enable local image manipulation. Once discovered, these directions can be applied to different images to produce semantically consistent edits, making our method suitable for practical applications. Experimental results on various datasets demonstrate that our method can produce semantic edits that are more localized and have better fidelity compared to the state-of-the-art. https://***/localdiff/. © 2024, CC BY.

关键词： Generative adversarial networks

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RobuSER: A Robustness Benchmark for speech Emotion Recognition 12

RobuSER: A Robustness Benchmark for Speech Emotion Recogniti...

引用

12th International Conference on Affective Computing and Intelligent Interaction, ACII 2024

作者： Petrogianni, Antonia Kapelonis, Lefteris Antoniou, Nikolaos Eleftheriou, Sofia Mitseas, Petros Sgouropoulos, Dimitris Katsamanis, Athanasios Giannakopoulos, Theodoros Narayanan, Shrikanth Behavioral Signal Technologies Athens Greece Behavioral Signal Technologies Institute for Language and Speech Processing Athena R.C Athens Greece Behavioral Signal Technologies Institute of Informatics and Telecommunications NCSR Demokritos Athens Greece Behavioral Signal Technologies SAIL University of Southern California Los Angeles United States

ISBN: (纸本)9798331516437

The recent surge in deep learning has improved speech Emotion Recognition (SER) model performance;however, ensuring robustness across diverse scenarios beyond the training dataset remains a problem. This challenge becomes pronounced in real-world situations characterized by noisy conditions, where model adaptability to unclean data is crucial. Despite ongoing efforts to develop noise-robust models, the lack of standardized evaluation protocols hampers fair comparisons among different models. This paper tackles this issue by introducing Robuser, a benchmarking procedure designed specifically for evaluating the robustness of SER models under noise. Robuser is a comprehensive open-source benchmark that can be applied to any speech dataset, focusing on diverse corruption types in two pivotal dimensions: additive background noise and various signal distortion corruptions, each in varying levels of severity. Furthermore, through the evaluation of a state-of-the-art SER model against this benchmark, we offer quantitative insights into the impact of the different corruption types and severity levels on performance. The baseline model reveals a notable performance degradation of up to 22.77% in Unweighted Accuracy (UA) and 20.32% in Weighted Accuracy (WA) on corrupted IEMOCAP, underscoring the substantial room for improvement in this domain. © 2024 IEEE.

关键词： speech enhancement

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