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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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AI Chest 4 All

AI Chest 4 All

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： P. Thammarach S. Khaengthanyakan S. Vongsurakrai P. Phienphanich P. Pooprasert A. Yaemsuk P. Vanichvarodom N. Munpolsri S. Khwayotha M. Lertkowit S. Tungsagunwattana C. Vijitsanguan S. Lertrojanapunya W. Noisiri I. Chiawiriyabunya N. Aphikulvanich C. Tantibundhit Faculty of Engineering Center of Excellence in Intelligence Informatics Speech and Language Technology and Service Innovation (CILS) Faculty of Medicine Cardiff University School of Medicine United Kingdom Udonthani Cancer Hospital Thailand Central Chest Institute of Thailand Department of Medical Services Thailand

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

AIChest4All is the name of the model used to label and screening diseases in our area of focus, Thailand, including heart disease, lung cancer, and tuberculosis. This is aimed to aid radiologist in Thailand especially in rural areas, where there is immense staff shortages. Deep learning is used in our methodology to classify the chest X-ray images from datasets namely, NIH set, which is separated into 14 observations, and the Montgomery and Shenzhen set, which contains chest X-ray images of patients with tuberculosis, further supplemented by the dataset from Udonthani Cancer hospital and the National Chest Institute of Thailand. The images are classified into six categories: no finding, suspected active tuberculosis, suspected lung malignancy, abnormal heart and great vessels, Intrathoracic abnormal findings, and Extrathroacic abnormal findings. A total of 201,527 images were used. Results from testing showed that the accuracy values of the categories heart disease, lung cancer, and tuberculosis were 94.11%, 93.28%, and 92.32%, respectively with sensitivity values of 90.07%, 81.02%, and 82.33%, respectively and the specificity values were 94.65%, 94.04%, and 93.54%, respectively. In conclusion, the results acquired have sufficient accuracy, sensitivity, and specificity values to be used. Currently, AIChest4All is being used to help several of Thailand's government funded hospitals, free of charge.

关键词： Lung Feature extraction X-ray imaging Machine learning Heart Cancer

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The Vicomtech-PRHLT speech Transcription Systems for the Iberspeech-RTVE 2018 speech to Text Transcription Challenge 4

The Vicomtech-PRHLT Speech Transcription Systems for the Ibe...

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4th International Conference on Advances in speech and language Technologies for Iberian languages, Iberspeech 2018

作者： Arzelus, Haritz Álvarez, Aitor Bernath, Conrad García, Eneritz Granell, Emilio Martínez-Hinarejos, Carlos D. Vicomtech Human Speech and Language Technology Group Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

This paper describes our joint submission to the Iberspeech-RTVE speech to Text Transcription Challenge 2018, which calls for automatic speech transcription systems to be evaluated in realistic TV shows. With the aim of building and evaluating systems, RTVE licensed around 569 hours of different TV programs, which were processed, re-aligned and revised in order to discard segments with imperfect transcriptions. This task reduced the corpus to 136 hours that we considered as nearly perfectly aligned audios and that we employed as in-domain data to train acoustic models. A total of 6 systems were built and presented to the evaluation challenge, three systems per condition. These recognition engines are different versions, evolution and configurations of two main architectures. The first architecture includes an hybrid LSTM-HMM acoustic model, where bidirectional LSTMs were trained to provide posterior probabilities for the HMM states. The language model corresponds to modified Kneser- Ney smoothed 3-gram and 9-gram models used for decoding and re-scoring of the lattices respectively. The second architecture includes an End-To-End based recognition system, which combines 2D convolutional neural networks as spectral feature extractor from spectrograms with bidirectional Gated Recurrent Units as RNN acoustic models. A modified Kneser-Ney smoothed 5-gram model was also integrated to re-score the E2E hypothesis. All the systems' outputs were then punctuated using bidirectional RNN models with attention mechanism and capitalized through recasing techniques. © 4th International Conference, Iberspeech 2018.

关键词： speech recognition

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language Recognition for Telephone and Video speech: The JHU HLTCOE Submission for NIST LRE17

Language Recognition for Telephone and Video Speech: The JHU...

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2018 Speaker and language Recognition Workshop, ODYSSEY 2018

作者： McCree, Alan Snyder, David Sell, Gregory Garcia-Romero, Daniel Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States

This paper presents our newest language recognition systems developed for NIST LRE17. For this challenging limited data multidomain task, we were able to get very good performance with our state-of-the-art DNN senone and bottleneck joint i-vector systems by effective utilization of all of the available training and development data. Data augmentation techniques were very valuable for this task, and our discriminative Gaussian classifier combined with naive fusion used all of the development data for system design rather than holding some out for separate back-end training. Finally, our newest research with discriminatively-trained DNN embeddings allowed us to replace i-vectors with more powerful x-vectors to further improve language recognition accuracy, resulting in very good LRE17 performance for this single system, our JHU HLTCOE site fusion primary submission, and the JHU MIT team submission. © ODYSSEY 2018. All rights reserved.

关键词： speech recognition

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Espresso: A Fast End-to-End Neural speech Recognition Toolkit

Espresso: A Fast End-to-End Neural Speech Recognition Toolki...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Yiming Wang Tongfei Chen Hainan Xu Shuoyang Ding Hang Lv Yiwen Shao Nanyun Peng Lei Xie Shinji Watanabe Sanjeev Khudanpur Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA ASLP@NPU School of Computer Science Northwestern Polytechnical University Xian China Information Sciences Institute University of Southern California Los Angeles CA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit FAIRSEQ. ESRESSO supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. Espresso achieves state-of-the-art ASR performance on the WSJ, Librispeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4-11x faster for decoding than similar systems (e.g. ESPNET).

关键词： Decoding speech recognition Training Computational modeling Gold Python Smoothing methods

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Exploring E2E speech recognition systems for new languages 4

Exploring E2E speech recognition systems for new languages

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4th International Conference on Advances in speech and language Technologies for Iberian languages, Iberspeech 2018

作者： Bernath, Conrad Álvarez, Aitor Arzelus, Haritz Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

Over the last few years, advances in both machine learning algorithms and computer hardware have led to significant improvements in speech recognition technology, mainly through the use of Deep Learning paradigms. As it was amply demonstrated in different studies, Deep Neural Networks (DNNs) have already outperformed traditional Gaussian Mixture Models (GMMs) at acoustic modeling in combination with Hidden Markov Models (HMMs). More recently, new attempts have focused on building end-to-end (E2E) speech recognition architectures, especially in languages with many resources like English and Chinese, with the aim of overcoming the performance of LSTM-HMM and more conventional systems. The aim of this work is first to present the different techniques that have been applied to enhance state-of-the-art E2E systems for American English using publicly available datasets. Secondly, we describe the construction of E2E systems for Spanish and Basque, and explain the strategies applied to overcome the problem of the limited availability of training data, especially for Basque as a low-resource language. At the evaluation phase, the three E2E systems are also compared with LSTM-HMM based recognition engines built and tested with the same datasets. © 4th International Conference, Iberspeech 2018.

关键词： Hidden Markov models

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How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

arXiv

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arXiv 2018年

作者： Ding, Shuoyang Duh, Kevin Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States

Using pre-trained word embeddings as input layer is a common practice in many natural language processing (NLP) tasks, but it is largely neglected for neural machine translation (NMT). In this paper, we conducted a systematic analysis on the effect of using pre-trained source-side monolingual word embedding in NMT. We compared several strategies, such as fixing or updating the embeddings during NMT training on varying amounts of data, and we also proposed a novel strategy called dual-embedding that blends the fixing and updating strategies. Our results suggest that pre-trained embeddings can be helpful if properly incorporated into NMT, especially when parallel data is limited or additional in-domain monolingual data is readily available. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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X-VECTORS: ROBUST DNN EMBEDDINGS FOR SPEAKER RECOGNITION

X-VECTORS: ROBUST DNN EMBEDDINGS FOR SPEAKER RECOGNITION

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IEEE International Conference on Acoustics, speech and Signal processing

作者： David Snyder Daniel Garcia-Romero Gregory Sell Daniel Povey Sanjeev Khudanpur Center for Language and Speech Processing & Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD 21218 USA

ISBN: (纸本)9781538646595

In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate between speakers, maps variable-length utterances to fixed-dimensional embeddings that we call x-vectors. Prior studies have found that embeddings leverage large-scale training datasets better than i-vectors. However, it can be challenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector extractor. However, the x-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.

关键词： speaker recognition deep neural networks data augmentation x-vectors speaker recognition Loudspeakers Reverberation

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Building corpora for single-channel speech separation across multiple domains

arXiv

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arXiv 2018年

作者： MacIejewski, Matthew Sell, Gregory Garcia-Perera, Leibny Paola Watanabe, Shinji Khudanpur, Sanjeev Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

To date, the bulk of research on single-channel speech separation has been conducted using clean, near-field, read speech, which is not representative of many modern applications. In this work, we develop a procedure for constructing high-quality synthetic overlap datasets, necessary for most deep learning-based separation frameworks. We produced datasets that are more representative of realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate standard methods on this data to demonstrate the shortcomings of current source-separation performance. We also demonstrate the value of a wide variety of data in training robust models that generalize well to multiple conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Source separation

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speech Enhancement via Deep Spectrum Image Translation Network 26

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019

作者： Kashani, Hamidreza Baradaran Jodeiri, Ata Goodarzi, Mohammad Mohsen Rezaei, Iman Sarraf Amirkabir University of Technology Electrical Engineering Faculty Tehran Iran College of Engineering University of Tehran School of Electrical Computer Engineering Tehran Iran Buein Zahra Technical University Department of Electrical and Computer Engineering Qazvin Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (纸本)9781728156637

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. © 2019 IEEE.

关键词： Audition

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