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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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Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static language Models

Methodological Insights in Detecting Subtle Semantic Shifts ...

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2023 Findings of the Association for Computational Linguistics: EMNLP 2023

作者： Hoeken, Sanne Alaçam, Özge Fokkens, Antske Sommerauer, Pia Computational Linguistics Dept. of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9798891760615

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction which relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts. © 2023 Association for Computational Linguistics.

关键词： Semantics

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Self-supervised Prosody Learning at Phoneme-level with Momentum Contrast for speech Synthesis

Self-supervised Prosody Learning at Phoneme-level with Momen...

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

作者： Zhao-Ci Liu Ya-Jun Hu Liping Chen Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei P.R.China iFLYTEK Research iFLYTEK Co. Ltd. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates leveraging large-scale speech data to enhance prosodic modeling in speech synthesis, and introduces a model named SP2MC which achieves self-supervised prosody learning at phoneme-level with momentum contrast. This model incorporates dual convolutional encoders for speech and linear predictive coding (LPC) residual inputs to generate phoneme-level embeddings, which are masked and processed by a Transformer model to produce prosody representations. Two supervision modules are employed to generate phoneme-level supervision from speech waveforms and residuals. Momentum contrast is utilized to manage negative sample selection in contrastive learning. Finally, the SP2MC representations are integrated into a Fastspeech2-based acoustic model for speech synthesis. Experimental results indicate that the naturalness of speech synthesized by the proposed method is significantly better than that of baselines.

关键词： Convolutional codes speech coding Convolution Predictive models speech enhancement Transformers Acoustics Data models Linear predictive coding Text to speech

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

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

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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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Wav2Nas: An Exploratory Approach to Nasalance Estimation in speech

Wav2Nas: An Exploratory Approach to Nasalance Estimation in ...

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International Symposium on Chinese Spoken language processing

作者： Rui Feng Yu-Ang Chen Yin-Long Liu Jia-Hong Yuan Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Interdisciplinary Research Center for Linguistic Sciences University of Science and Technology of China Hefei

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Nasalance, defined as the ratio of nasal energy to total acoustic energy during speech, is an important metric in speech science and clinical phonetics. Measurement of nasalance, how-ever, requires specialized equipment, which has severely limited its widespread applications. In this study, we explored methods of predicting nasalance from speech waveforms. We designed an oral-nasal separation mask with thermal flow sensors to record the airflows from the mouth and nose separately during speech production, alongside a microphone recording speech sounds. Nasalance was calculated from the oral and nasal airflows, and multilayer perceptron (MLP) models were trained to predict nasalance from speech waveforms. We compared Mel-spectrogram, Mel Frequency Cepstral Coefficients (MFCC), and Wav2vec 2.0 features as inputs to MLPs. The results demonstrated that the Wav2vec 2.0-based features have the highest Pearson Product Moment Correlation Coefficient (PPMC) of 0.7459, outperforming both the Mel-spectrogram and MFCC baselines. These findings emphasize the potential of leveraging pre-trained deep learning models such as Wav2vec 2.0 to predict nasalance directly from raw audio data, reducing reliance on expensive instruments and improving diagnostic capabilities in speech pathology. Moreover, this paper under-scores the promise of deep learning methods in advancing clinical assessment and opens up new avenues for applying compu-tational techniques to better understand and treat speech disorders.

关键词： Deep learning Pathology Nose Production Thermal sensors Predictive models Phonetics Recording Mel frequency cepstral coefficient speech processing

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Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

arXiv

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arXiv 2024年

作者： Senger, Elena Zhang, Mike van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Department of Computer Science IT University of Copenhagen Denmark Fraunhofer Center for International Management and Knowledge Economy IMW Germany

Recent years have brought significant advances to Natural language processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this emerging field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction and classification. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification. © 2024, CC BY.

关键词： Terminology

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KARRIEREWEGE: A Large Scale Career Path Prediction Dataset

arXiv

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arXiv 2024年

作者： Senger, Elena Campbell, Yuri van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Fraunhofer Center for International Management and Knowledge Economy IMW Germany Department of Computer Science IT University of Copenhagen Denmark

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data. © 2024, CC BY.

关键词： Prediction models

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A Novel Multimodal Sentiment Analysis Model Based on Gated Fusion and Multi-Task Learning

A Novel Multimodal Sentiment Analysis Model Based on Gated F...

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

作者： Xin Sun Xiangyu Ren Xiaohao Xie School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Applications Research Center on High Volume Language Information Processing and Cloud Computing

Sentiment analysis is an important research area in Natural language processing (NLP). With the explosion of multimodal data, Multimodal Sentiment Analysis (MSA) attracts more and more attention in recent years. How to Effectively harnessing the interplay between diverse modalities is paramount to achieving comprehensive fusion of MSA. However, current research predominantly emphasizes modality interaction, while overlooking unimodal information, thus neglecting the inherent disparities between modalities. To address these issues, we propose a novel model for multimodal sentiment analysis based on gated fusion and multi-task learning. The model adopts multi-task learning to concurrently address both multimodal and unimodal sentiment analysis tasks. Specifically, for the multimodal task, we leverage cross-modal Transformers with gating mechanisms to facilitate modality fusion. Subsequently, the fused representations are harnessed to generate sentiment labels for the unimodal tasks. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our model outperforms the existing methods and achieves the state-of-the art performance.

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Wiktionary Normalization of Translations and Morphological Information 28

Wiktionary Normalization of Translations and Morphological I...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Wu, Winston Yarowsky, David Department of Computer Science Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9781952148279

We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation—clipping, contraction, and eye dialect—and improve upon a standard attention baseline by using copy attention. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Translation (languages)

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VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

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

作者： Ao Zhang He Wang Pengcheng Guo Yihui Fu Lei Xie Yingying Gao Shilei Zhang Junlan Feng Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China China Mobile Research Institute Beijing China

The performance of the keyword spotting (KWS) system based on audio modality, commonly measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. Therefore, audio-visual keyword spotting, which leverages complementary relationships over multiple modalities, has recently gained much attention. However, current studies mainly focus on combining the exclusively learned representations of different modalities, instead of exploring the modal relationships during each respective modeling. In this paper, we propose a novel visual modality enhanced end-to-end KWS framework (VE-KWS), which fuses audio and visual modalities from two aspects. The first one is utilizing the speaker location information obtained from the lip region in videos to assist the training of multi-channel audio beamformer. By involving the beamformer as an audio enhancement module, the acoustic distortions, caused by the far field or noisy environments, could be significantly suppressed. The other one is conducting cross-attention between different modalities to capture the inter-modal relationships and help the representation learning of each modality. Experiments on the MSIP challenge corpus show that our proposed model achieves a 2.79% false rejection rate and a 2.95% false alarm rate on the Eval set, resulting in a new SOTA performance compared with the top-ranking systems in the ICASSP2022 MISP challenge.

关键词： Training Representation learning Visualization Fuses Lips Noise reduction Real-time systems

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