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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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Transfer learning for automated responses to the BDI questionnaire

Transfer learning for automated responses to the BDI questio...

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2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

作者： Spartalis, Christoforos Drosatos, George Arampatzis, Avi Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

This paper describes the participation of the DUTH-ATHENA team of Democritus University of Thrace and Athena Research center in the eRisk 2021 task, which focuses on measuring the level of depression based on Reddit users' posts. We address this task using both feature-based and fine-tuning strategies for applying BERT-based representations. In the feature-based approaches, we examine the possibilities of a SBERT model based on RoBERTa, pre-trained on Natural language Inference (NLI) data and fine-tuned on STSb dataset to leverage transfer learning to depression-level estimation, and we achieve promising results. One of our runs ranks first in Average Hit Rate (AHR), while the others rank among the best four in the other evaluation metrics. Also, for the fine-tuning approach, we propose two predictive models that are built upon RoBERTa, which provide directions for future optimizations. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social networking (online)

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SL-REDU GSL: A Large Greek Sign language Recognition Corpus

SL-REDU GSL: A Large Greek Sign Language Recognition Corpus

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

作者： Katerina Papadimitriou Galini Sapountzaki Kyriaki Vasilaki Eleni Efthimiou Stavroula-Evita Fotinea Gerasimos Potamianos 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

We present a large multi-signer video corpus for the Greek Sign language (GSL), suitable for the development and evaluation of GSL recognition algorithms. The database has been collected as part of the “SL-ReDu” project that focuses on the education use-case of systematic teaching of GSL as a second language (L2). The project aims to assist this process by allowing self-monitoring and objective assessment of GSL learners’ productions through the use of recognition technology, thus requiring suitable data resources relevant to the aforementioned use-case. To this end, we present the SL-ReDu GSL corpus, an extensive RGB+D video collection of 21 informants with a duration of 36 hours, recorded under studio conditions, consisting of: (i) isolated signs; (ii) continuous signing (annotated at the sentence level); and (iii) fingerspelling of words. We provide a detailed description of the design and acquisition methods used to develop it, along with corpus statistics and a comparison to existing sign language datasets. The SL-ReDu GSL corpus, as well as proposed frameworks for recognition experiments on it, are publicly available at https://***/corpus.

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Decoding speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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2024 IEEE Signal processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

arXiv

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arXiv 2023年

作者： Li, Shuyue Stella Xu, Beining Zhang, Xiangyu Liu, Hexin Chao, Wenhan Garcia, Leibny Paola Center for Language and Speech Processing Johns Hopkins University United States School of Computer Science and Engineering Beihang University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature representations. Using automatic speech recognition (ASR) as a downstream task, we analyze the effect of model size, training objectives, and model architecture on the models’ performance as a feature extractor for a set of topologically diverse corpora. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations using deep generalized canonical correlation analysis. Results show the contrastive loss in the wav2vec2.0 objective facilitates more effective cross-lingual feature extraction. There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings. The proposed metric is an effective indicator of the quality of the representations and can be useful for model selection.1 © 2023, CC BY.

关键词： Topology

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Quality-Aware End-to-End Audio-Visual Neural Speaker Diarization

arXiv

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arXiv 2024年

作者： He, Mao-Kui Du, Jun Niu, Shu-Tong Liu, Qing-Feng Lee, Chin-Hui National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Anhui Hefei China IFlytek Hefei Anhui China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

In this paper, we propose a quality-aware end-to-end audio-visual neural speaker diarization framework, which comprises three key techniques. First, our audio-visual model takes both audio and visual features as inputs, utilizing a series of binary classification output layers to simultaneously identify the activities of all speakers. This end-to-end framework is meticulously designed to effectively handle situations of overlapping speech, providing accurate discrimination between speech and non-speech segments through the utilization of multimodal information. Next, we employ a quality-aware audio-visual fusion structure to address signal quality issues for both audio degradations, such as noise, reverberation and other distortions, and video degradations, such as occlusions, off-screen speakers, or unreliable detection. Finally, a cross attention mechanism applied to multi-speaker embedding empowers the network to handle scenarios with varying numbers of speakers. Our experimental results, obtained from various data sets, demonstrate the robustness of our proposed techniques in diverse acoustic environments. Even in scenarios with severely degraded video quality, our system attains performance levels comparable to the best available audio-visual systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reverberation

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

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

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

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

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.

关键词： Distributed ledger Smart contracts Prototypes Systems architecture Companies Fabrics Blockchains

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Are Clinical T5 Models Better for Clinical Text?

arXiv

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arXiv 2024年

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 United States Malone Center for Engineering in Healthcare Johns Hopkins University United States

Large language models with a transformerbased 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, CC BY-NC-ND.

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PQLM - Multilingual Decentralized Portable Quantum language Model

PQLM - Multilingual Decentralized Portable Quantum Language ...

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

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

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.

关键词： Training Sentiment analysis Social networking (online) Computational modeling Signal processing Stability analysis Servers

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Corrective Retrieval Augmented Generation

arXiv

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arXiv 2024年

作者： Yan, Shi-Qi Gu, Jia-Chen Zhu, Yun Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Computer Science University of California Los Angeles United States Google DeepMind United Kingdom

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return suboptimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query language

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