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Acoustics, speech, and Signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Thomas Hanke Lutz König Reiner Konrad Maria Kopf Marc Schulder Rosalee Wolfe Institute of German Sign Language Universität Hamburg Hamburg Germany Institute for Language and Speech Processing Athena Research Center Athens Greece

We introduce EASIER Notation, a gloss-based scripting language to describe sign language content to be signed by an avatar and describe the functionality a lexical database for a sign language needs to provide in order to fully support the notation approach. In addition, we present the prototype of a text editor supporting EASIER Notation for human post-editing of machine translation output as well as pre-scribing signed utterances from scratch.

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Multiword Expressions between the Corpus and the Lexicon: Universality, Idiosyncrasy and the Lexicon-Corpus Interface

Multiword Expressions between the Corpus and the Lexicon: Un...

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2024 Joint Workshop on Multiword Expressions and Universal Dependencies, MWE-UD 2024

作者： Mititelu, Verginica Barbu Giouli, Voula Evang, Kilian Zeman, Daniel Osenova, Petya Tiberius, Carole Krek, Simon Markantonatou, Stella Stoyanova, Ivelina Stankovic, Ranka Chiarcos, Christian Romanian Academy Research Institute for Artificial Intelligence Romania Insitute for Language and Speech Processing Athena Research Centre Greece Heinrich Heine University Düsseldorf Germany ÚFAL MFF Charles University Czech Republic Institute of Information and Communication Technologies BAS Leiden University Netherlands Jožef Stefan Institute Slovenia Institute for Bulgarian Language BAS Bulgaria University of Belgrade Serbia University of Augsburg Germany

ISBN: (纸本)9782493814203

We present ongoing work towards defining a lexicon-corpus interface to serve as a benchmark in the representation of multiword expressions (of various types – nominal, verbal, etc.) in dedicated lexica and the linking of these entries to their corpus occurrences. The final aim is the harnessing of such resources for the automatic identification of multiword expressions in a text. The involvement of several natural languages aims at the universality of a solution not centered on a particular language, and also accommodating idiosyncrasies. Challenges in the lexicographic description of multiword expressions are discussed, the current status of lexica dedicated to this linguistic phenomenon is outlined, as well as the solution we envisage for creating an ecosystem of interlinked lexica and corpora containing and, respectively, annotated with multiword expressions. © European language Resources Association: CC BY-NC 4.0.

关键词： Signal encoding

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Emotion-Aware speech Popularity Prediction: A Use-Case on TED Talks 12

Emotion-Aware Speech Popularity Prediction: A Use-Case on TE...

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

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

ISBN: (纸本)9798331516437

In the context of the ever-growing influence of social media, understanding and predicting the popularity of content has become crucial for creators and marketers alike. Our research addresses this need by introducing a method to forecast the success of oral presentations, focusing on the nuanced use of paralinguistic features and insights derived from speech emotion recognition models. This innovative approach is designed to enhance verbal communication skills by providing public speakers with targeted feedback. We leverage a dataset of 2,462 TED talk videos, complete with metadata such as user comments, tags, and views, to establish a set of four objective metrics for determining presentation popularity. These metrics form the foundation of our analysis, enabling us to evaluate the efficacy of our predictive methodology. By integrating audio-based emotional cues with text-based content analysis we showcase the capability of the proposed speech analytics system to capture user assessments of presentation quality. This research highlights the role of emotional expression in speech as a component of content's appeal, advocating for a broader analytical perspective beyond just text-only analysis. It suggests new directions for improving the impact of public speaking and calls for further investigation into multimodal content analysis, aiming to deepen our understanding of audience engagement on social media and content delivery platforms. © 2024 IEEE.

关键词： speech enhancement

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Exploring language-Agnostic speech Representations Using Domain Knowledge for Detecting Alzheimer’s Dementia

Exploring Language-Agnostic Speech Representations Using Dom...

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

作者： Zehra Shah Shi-Ang Qi Fei Wang Mahtab Farrokh Mashrura Tasnim Eleni Stroulia Russell Greiner Manos Plitsis Athanasios Katsamanis Department of Computing Science University of Alberta Edmonton Canada Institute for Language and Speech Processing Athena Research Center Greece

We explore ways to use speech data to screen for indications of Alzheimer’s dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal processing Grand Challenge, which involves extrapolating from models learned from English speech samples, to Greek speech samples, to determine which subjects have AD. By using acoustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings.

关键词： Signal processing Linguistics Acoustics speech processing Alzheimer's disease Testing

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INVESTIGATING PERSONALIZATION METHODS IN TEXT TO MUSIC GENERATION

arXiv

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arXiv 2023年

作者： Plitsis, Manos Kouzelis, Theodoros Paraskevopoulos, Georgios Katsouros, Vassilis Panagakis, Yannis 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 constructs more easily than melody. The code, dataset, and example material of this study are open to the research community. © 2023, CC BY.

关键词： Petroleum reservoir evaluation

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The Corpus AIKIA: using ranking annotation for Offensive language Detection in Modern Greek 30

The Corpus AIKIA: using ranking annotation for Offensive Lan...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Markantonatou, Stella Stamou, Vivian Christodoulou, Christina Apostolopoulou, Georgia Balas, Antonis Ioannakis, George Institute for Language and Speech Processing National Centre for Scientific Research "Demokritos" Athens Greece Artemidos 6 & Epidavrou Maroussi151 25 Greece Archimedes Athena R.C. Panepistimioupolis Ilissia AthensGR-16122 Greece Department of Informatics and Telecommunications NKUA Informatics & Telecommunications Agia Paraskevi15310 Greece

ISBN: (纸本)9782493814104

We introduce a new corpus, named AIKIA, for Offensive language Detection (OLD) in Modern Greek (EL). EL is a less-resourced language regarding OLD. AIKIA offers free access to annotated data leveraged from EL Twitter and fiction texts using the lexicon of offensive terms, ERIS, that originates from HurtLex. AIKIA has been annotated for offensive values with the Best Worst Scaling (BWS) method, which is designed to avoid problems of categorical and scalar annotation methods. BWS assigns continuous offensive scores in the form of floating point numbers instead of binary arithmetical or categorical values. AIKIA's performance in OLD was tested by fine-tuning a variety of pre-trained language models in a binary classification task. Experimentation with a number of thresholds showed that the best mapping of the continuous values to binary labels should occur at the range [0.5-0.6] of BWS values and that the pre-trained models on EL data achieved the highest Macro-F1 scores. Greek-Media-BERT outperformed all models with a threshold of 0.6 by obtaining a Macro-F1 score of 0.92. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Digital arithmetic

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Adapted Multimodal Bert with Layer-Wise Fusion for Sentiment Analysis

Adapted Multimodal Bert with Layer-Wise Fusion for Sentiment...

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

作者： Odysseas S. Chlapanis Georgios Paraskevopoulos Alexandros Potamianos National Technical University of Athens Athens Greece Athena Research Center Institute for Language and Speech Processing Athens Greece

Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose Adapted Multimodal BERT (AMB), a BERT-based architecture for multimodal tasks that uses a combination of adapter modules and intermediate fusion layers. The adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. During the adaptation process the pre-trained language model parameters remain frozen, allowing for fast, parameter-efficient training. In our ablations we see that this approach leads to efficient models, that can outperform their fine-tuned counterparts and are robust to input noise. Our experiments on sentiment analysis with CMU-MOSEI show that AMB outperforms the current state-of-the-art across metrics, with 3.4% relative reduction in the resulting error and 2.1% relative improvement in 7−class classification accuracy.

关键词： Training Adaptation models Sentiment analysis Costs Bit error rate Signal processing Transformers

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A Machine Learning Approach for MIDI to Guitar Tablature Conversion 19

A Machine Learning Approach for MIDI to Guitar Tablature Con...

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19th Sound and Music Computing Conference, SMC 2022

作者： Kaliakatsos-Papakostas, Maximos Bastas, Grigoris Makris, Dimos Herremans, Dorrien Katsouros, Vassilis Maragos, Petros Institute for Language and Speech Processing Athena R.C. Athens Greece School of Electrical and Computer Engineering NTUA Athens Greece Department of Computer Science and Design Pillar SUTD Singapore

ISBN: (纸本)9782958412609

Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the history of guitar playing, specific chord fingerings have been developed across different musical styles that facilitate common idiomatic voicing combinations and motion between them. This paper presents a method for assigning guitar tablature notation to a given MIDI-based musical part (possibly consisting of multiple polyphonic tracks), i.e. no information about guitar-idiomatic expressional characteristics is involved (e.g. bending etc.) The current strategy is based on machine learning and requires a basic assumption about how much fingers can stretch on a fretboard;only standard 6-string guitar tuning is examined. The proposed method also examines the transcription of music pieces that was not meant to be played or could not possibly be played by a guitar (e.g. potentially a symphonic orchestra part), employing a rudimentary method for augmenting musical information and training/testing the system with artificial data. The results present interesting aspects about what the system can achieve when trained on the initial and augmented dataset, showing that the training with augmented data improves the performance even in simple, e.g. monophonic, cases. Results also indicate weaknesses and lead to useful conclusions about possible improvements. Copyright: © 2022 First author et al.

关键词： Machine learning

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SciNoBo: A Hierarchical Multi-Label Classifier of Scientific Publications

arXiv

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arXiv 2022年

作者： Gialitsis, Nikolaos Kotitsas, Sotiris Papageorgiou, Haris Athena Research and Innovation Center Institute for Language and Speech Processing Athens Greece

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neuralnetwork baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications. © 2022, CC BY.

关键词： Classification (of information)

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Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling

arXiv

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arXiv 2023年

作者： Kouzelis, Theodoros Paraskevopoulos, Georgios Katsamanis, Athanasios Katsouros, Vassilis Institute for Language and Speech Processing Athena Research Center Greece School of ECE National Technical University of Athens Athens Greece

The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech aligners, hindering the use of automatic approaches. In this work, we propose a simple and effective modification of alignment graph construction of CTC-based models using Weighted Finite State Transducers. The proposed weakly-supervised approach alleviates the need for verbatim transcription of speech disfluencies for forced alignment. During the graph construction, we allow the modeling of common speech disfluencies, i.e. repetitions and omissions. Further, we show that by assessing the degree of audio-text mismatch through the use of Oracle Error Rate, our method can be effectively used in the wild. Our evaluation on a corrupted version of the TIMIT test set and the UCLASS dataset shows significant improvements, particularly for recall, achieving a 23-25% relative improvement over our baselines. © 2023, CC BY.

关键词： Alignment

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