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Sample-Efficient Unsupervised Domain Adaptation of speech Recognition Systems: A Case Study for Modern Greek

IEEE/ACM Transactions on Audio speech and language processing 2024年 32卷 286-299页

作者： Paraskevopoulos, Georgios Kouzelis, Theodoros Rouvalis, Georgios Katsamanis, Athanasios Katsouros, Vassilis Potamianos, Alexandros National Technical University of Athens Graduate School of Electrical and Computer Engineering Athens10682 Greece Athena Research Center Institute for Speech and Language Processing Marousi15125 Greece National Technical University of Athens Faculty of Electrical and Computer Engineering Athens10682 Greece

Modern speech recognition systems exhibit rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where the diversity of training data is limited. In this work, we propose M2DS2, a simple and sample-efficient fine-tuning strategy for large pre-trained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a 120-hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments, we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines. © 2014 IEEE.

关键词： speech recognition

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Data management in Ethereum DApps: A cost and performance analysis

Data management in Ethereum DApps: A cost and performance an...

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

The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional ones, and evaluate them in terms of storage cost and retrieval latency while considering the impact of recent hard forks. More precisely, we elaborate on the use of Smart Contract (SC) storage as a data store for DApps and in view of the prohibitive cost inherent to this approach, we present and compare methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. In addition, we study hybrid schemes by using IPFS and Swarm as storage platforms and Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. The upload and retrieval performance of these platforms and the cost associated with storing (on-chain) the identifiers of the uploaded data are of utmost importance and therefore are investigated in detail. © 2023

关键词： Smart contract

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Are Clinical T5 Models Better for Clinical Text? 4

Are Clinical T5 Models Better for Clinical Text?

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4th Machine Learning for Health Symposium, ML4H 2024

作者： Li, Yahan Harrigian, Keith Zirikly, Ayah Dredze, Mark Department of Computer Science University of Southern California United States Department of Computer Science Johns Hopkins University United States Center for Language and Speech Processing Whiting School of Engineering Johns Hopkins University Malone Center for Engineering in Healthcare Johns Hopkins University United States

Large language models with a transformer-based encoder/decoder architecture, such as T5 (Raffel et al., 2023), have become standard platforms for supervised tasks. To bring these technologies to the clinical domain, recent work has trained new (Lehman et al., 2023) or adapted existing (Lu et al., 2022) models to clinical data. However, the evaluation of these clinical T5 models and comparison to other models has been limited. Are the clinical T5 models better choices than FLAN-tuned (Chung et al., 2022a) generic T5 models? Do they generalize better to new clinical domains that differ from the training sets? We comprehensively evaluate these models across several clinical tasks and domains. We find that clinical T5 models provide marginal improvements over existing models, and perform worse when evaluated on different domains. Our results inform future choices in developing clinical LLMs. © 2024 Y. Li, K. Harrigian, A. Zirikly & M. Dredze.

关键词： Domain Generalization language Models Pre-training

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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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Greek Sign language Recognition for the SL-ReDu Learning Platform 7

Greek Sign Language Recognition for the SL-ReDu Learning Pla...

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7th Workshop on Sign language Translation and Avatar Technology: The Junction of the Visual and the Textual Challenges and Perspectives, SLTAT 2022

作者： Papadimitriou, Katerina Potamianos, Gerasimos Sapountzaki, Galini Goulas, Theodor Efthimiou, Eleni Fotinea, Stavroula-Evita Maragos, Petros Department of Electrical & Computer Engineering University of Thessaly Volos Greece Department of Special Education University of Thessaly Volos Greece Institute for Language & Speech Processing Athena Research & Innovation Center Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9791095546825

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively. © European language Resources Association (ELRA), licensed under CC-BY-NC 4.0.

关键词： Deep learning

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PQLM - Multilingual Decentralized Portable Quantum language Model 48

PQLM - Multilingual Decentralized Portable Quantum Language ...

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

作者： Li, Shuyue Stella Zhang, Xiangyu Zhou, Shu Shu, Hongchao Liang, Ruixing Liu, Hexin Garcia, Leibny Paola Hong Kong University of Science and Technology Department of Physics Hong Kong Nanyang Technological University School of Electrical and Electronic Engineering Singapore Johns Hopkins University Center for Language and Speech Processing United States Johns Hopkins University Human Language Technology Center of Excellence United States

ISBN: (纸本)9781728163277

With careful manipulation, malicious agents can reverse engineer private information encoded in pre-trained language models. Security concerns motivate the development of quantum pre-training. In this work, we propose a highly portable quantum language model (PQLM) that can easily transmit information to downstream tasks on classical machines. The framework consists of a cloud PQLM built with random Variational Quantum Classifiers (VQC) and local models for downstream applications. We demonstrate the ad hoc portability of the quantum model by extracting only the word embeddings and effectively applying them to downstream tasks on classical machines. Our PQLM exhibits comparable performance to its classical counterpart on both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy) metrics. We also perform ablation studies on the factors affecting PQLM performance to analyze model stability. Our work establishes a theoretical foundation for a portable quantum pre-trained language model that could be trained on private data and made available for public use with privacy protection guarantees. © 2023 IEEE.

关键词： Federated Learning language Modeling Model Portability Quantum Machine Learning

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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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Eye movement patterns are similar during accurate multiple-target tracking 15

Eye movement patterns are similar during accurate multiple-t...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Bagha, Kamyar Kamkar, Shiva Moghaddam, Hamid Abrishami Oksama, Lauri Li, Jie Hyönä, Jukka Computer Engineering Department Khatam University Tehran Iran Laboratory Faculty of Electrical Engineering K.N.Toosi University of Technology Tehran Iran Tehran Iran Department of Psychology and Speech-Language Pathology University of Turku Turku Finland Center for Cognition and Brain Disorders Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350378245

Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication. © 2024 IEEE.

关键词： Eye movements

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Predicting Early Dropouts of an Active and Healthy Ageing App

arXiv

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arXiv 2023年

作者： Perifanis, Vasileios Michailidi, Ioanna Stamatelatos, Giorgos Drosatos, George Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022 (***), part of IUPESM WC 2022. We have processed the given database and generated seven datasets. We used pre-processing techniques to construct classification models that predict the adherence of users using dynamic and static features. We submitted 11 official runs and our results show that machine learning algorithms can provide high-quality adherence predictions. Based on the results, the dynamic features positively influence a model’s classification performance. Due to the imbalanced nature of the dataset, we employed oversampling methods such as SMOTE and ADASYN to improve the classification performance. The oversampling approaches led to a remarkable improvement of 10%. Our methods won first place in the IFMBE Scientific Challenge 2022. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson's disease 46

Concurrent validity of instrumented insoles measuring gait a...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Watkinson, Sophia A. Anderson, Anthony Caiola, Michael Eguren, David Gonzalez, Michael Velazquez, Laureano Moro Dehak, Najim Motley, Chelsea Moukheiber, Emile Mills, Kelly Muir, Brittney Butala, Ankur Kontson, Kimberly Us Food and Drug Administration Division of Biomedical Physics Silver SpringMD United States Johns Hopkins University Center for Language and Speech Processing BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University School of Medicine Department of Neurology BaltimoreMD United States Center for Limb Loss and Mobility Department of Veterans Affairsy SeattleWA United States

ISBN: (纸本)9798350371499

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M;mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson's disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson's correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems. © 2024 IEEE.

关键词： Gait analysis

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