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Exploiting Extended Reality Technologies for Educational Microscopy 17th

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Exploiting Extended Reality Technologies for Educational Mic...

17th International Conference on Virtual Reality and Augmented Reality, EuroVR 2020

作者： Theodoropoulou, Helena G. Kiourt, Chairi Lalos, Aris S. Koutsoudis, Anestis Paxinou, Evgenia Kalles, Dimitris Pavlidis, George Industrial Systems Institute Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Patra Greece Institute for Language and Speech Processing Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Xanthi Greece School of Science and Technology Hellenic Open University Patra Greece

ISBN: (纸本)9783030626549

Exploiting extended reality technologies in laboratory training enhances both teaching and learning experiences. It complements the existing traditional learning/teaching methods related to science, technology, engineering, arts and mathematics. In this work, we use extended reality technologies to create an interactive learning environment with dynamic educational content. The proposed learning environment can be used by students of all levels of education, to facilitate laboratory-based understanding of scientific concepts. We introduce a low-cost and user-friendly multi-platform system for mobile devices which, when coupled with edutainment dynamics, simulation, extended reality and natural hand movements sensing technologies such as hand gestures with virtual triggers, is expected to engage users and prepare them efficiently for the actual on-site laboratory experiments. The proposed system is evaluated by a group of experts and the results are analyzed in detail, indicating the positive attitude of the evaluators towards the adoption of the proposed system in laboratory educational procedures. We conclude the paper by highlighting the capabilities of extended reality and dynamic content management in educational microscopy procedures. © 2020, Springer Nature Switzerland AG.

关键词： Laboratories

Software in the natural world: A computational approach to hierarchical emergence

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arXiv 2024年

作者： Rosas, Fernando E. Geiger, Bernhard C. Luppi, Andrea I. Seth, Anil K. Polani, Daniel Gastpar, Michael Mediano, Pedro A.M. Department of Informatics University of Sussex United Kingdom Sussex Centre for Consciousness Science and Sussex AI University of Sussex United Kingdom Center for Psychedelic Research and Centre for Complexity Science Department of Brain Science Imperial College London United Kingdom Center for Eudaimonia and Human Flourishing University of Oxford United Kingdom Know-Center GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Montreal Neurological Institute McGill University Canada Department of Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer and Communication Sciences EPFL Lausanne Switzerland Department of Computing Imperial College London United Kingdom Division of Psychology and Language Sciences University College London United Kingdom

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels;however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this important limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalisation of how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework reveals a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical Physics

Wake Word Detection with Alignment-Free Lattice-Free MMI

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arXiv 2020年

作者： Wang, Yiming Lv, Hang Povey, Daniel Xie, Lei Khudanpur, Sanjeev Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Xiaomi Inc. Beijing China ASLP@NPU School of Computer Science Northwestern Polytechnical University Xi’an China

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word;(ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance;(iii) we present an FST-based decoder to perform online detection. We evaluate our methods on two real data sets, showing 50%–90% reduction in false rejection rates at prespecified false alarm rates over the best previously published figures, and re-validate them on a third (large) data set. Copyright © 2020, The Authors. All rights reserved.

关键词： Wakes

A call for prudent choice of subword merge operations in neural machine translation

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arXiv 2019年

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

Most neural machine translation systems are built upon subword units extracted by methods such as Byte-Pair Encoding (BPE) or wordpiece. However, the choice of number of merge operations is generally made by following existing recipes. In this paper, we conduct a systematic exploration on different numbers of BPE merge operations to understand how it interacts with the model architecture, the strategy to build vocabularies and the language pair. Our exploration could provide guidance for selecting proper BPE configurations in the future. Most prominently: we show that for LSTM-based architectures, it is necessary to experiment with a wide range of different BPE operations as there is no typical optimal BPE configuration, whereas for Transformer architectures, smaller BPE size tends to be a typically optimal choice. We urge the community to make prudent choices with subword merge operations, as our experiments indicate that a sub-optimal BPE configuration alone could easily reduce the system performance by 3–4 BLEU points. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational linguistics

Weakly-Supervised Depression Detection in speech Through Self-Learning Based Label Correction

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IEEE Transactions on Audio, Speech and Language Processing

IEEE Transactions on Audio, speech and language processing 2025年 33卷 748-758页

作者： Yanfei Sun Yuanyuan Zhou Xinzhou Xu Jin Qi Feiyi Xu Zhao Ren Björn W. Schuller School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Wuxi University Wuxi China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China Cognitive Systems Lab University of Bremen Bremen Germany Chair of Health Informatics Technische Universität München (TUM) München Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany GLAM – the Group on Language Audio & Music Imperial College London London U.K.

Automated Depression Detection (ADD) in speech aims to automatically estimate one's depressive attributes through artificial intelligence tools towards spoken signals. Nevertheless, existing speech-based ADD works fail to sufficiently consider weakly-supervised cases with inaccurate labels, which may typically appear in intelligent mental health. In this regard, we propose the Self-Learning-based Label Correction (SLLC) approach for weakly-supervised depression detection in speech. The proposed approach employs a self-learning manner connecting a label correction module and a depression detection module. Within the approach, the label correction module fuses likelihood-ratio-based and prototype-based label correction strategies in order to effectively correct the inaccurate labels, while the depression detection module aims at detecting depressed samples through a 1D convolutional recurrent neural network with multiple types of losses. The experimental results on two depression detection corpora show that our proposed SLLC approach performs better compared with existing state-of-the-art speech-based depression detection approaches, in the case of weak supervision with inaccurate labels for depression detection in speech.

关键词： Depression Feature extraction Training speech processing Prototypes Noise measurement Telecommunications Sun Internet of Things Weak supervision

How Phonotactics Affect Multilingual and Zero-Shot ASR Performance

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How Phonotactics Affect Multilingual and Zero-Shot ASR Perfo...

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

作者： Siyuan Feng Piotr Żelasko Laureano Moro-Velázquez Ali Abavisani Mark Hasegawa-Johnson Odette Scharenborg Najim Dehak Multimedia Computing Group Delft University of Technology Delft The Netherlands Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA Johns Hopkins University Baltimore MD USA University of Illinois at Urbana-Champaign IL USA

ISBN: (纸本)9781728176055;9781728176062

The idea of combining multiple languages’ recordings to train a single automatic speech recognition (ASR) model brings the promise of the emergence of universal speech representation. Recently, a Transformer encoder-decoder model has been shown to leverage multilingual data well in IPA transcriptions of languages presented during training. However, the representations it learned were not successful in zero-shot transfer to unseen languages. Because that model lacks an explicit factorization of the acoustic model (AM) and language model (LM), it is unclear to what degree the performance suffered from differences in pronunciation or the mismatch in phono-tactics. To gain more insight into the factors limiting zero-shot ASR transfer, we replace the encoder-decoder with a hybrid ASR system consisting of a separate AM and LM. Then, we perform an extensive evaluation of monolingual, multilingual, and crosslingual (zero-shot) acoustic and language models on a set of 13 phonetically diverse languages. We show that the gain from modeling crosslingual phonotactics is limited, and imposing a too strong model can hurt the zero-shot transfer. Furthermore, we find that a multilingual LM hurts a multilingual ASR system’s performance, and retaining only the target language’s phonotactic data in LM training is preferable.

关键词： Training Limiting Conferences Acoustics Data models MONOS devices speech processing

That sounds familiar: An analysis of phonetic representations transfer across languages

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arXiv 2020年

作者： Zelasko, Piotr Velazquez, Laureano Moro Johnson, Mark Hasegawa Scharenborg, Odette Dehak, Najim Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Ece Department and Beckman Institute University of Illinois Urbana-Champaign United States Multimedia Computing Group Delft University of Technology Delft Netherlands

Only a handful of the worlds languages are abundant with the resources that enable practical applications of speech processing technologies. One of the methods to overcome this problem is to use the resources existing in other languages to train a multilingual automatic speech recognition (ASR) model, which, intuitively, should learn some universal phonetic representations. In this work, we focus on gaining a deeper understanding of how general these representations might be, and how individual phones are getting improved in a multilingual setting. To that end, we select a phonetically diverse set of languages, and perform a series of monolingual, multilingual and crosslingual (zero-shot) experiments. The ASR is trained to recognize the International Phonetic Alphabet (IPA) token sequences. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting, where the model, among other errors, considers Javanese as a tone language. Notably, as little as 10 hours of the target language training data tremendously reduces ASR error rates Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages-an encouraging result for the lowresource speech community. Copyright © 2020, The Authors. All rights reserved.

关键词： speech recognition

Zero Resource Speaking Rate Estimation from Change Point Detection of Syllable-like Units 44

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Zero Resource Speaking Rate Estimation from Change Point Det...

44th IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2019

作者： Nayak, Shekhar Bhati, Saurabhchand Rama Murty, K. Sri Department of Electrical Engineering Indian Institute of Technology Hyderabad India Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9781479981311

Speaking rate is an important attribute of the speech signal which plays a crucial role in the performance of automatic speech processing systems. In this paper, we propose to estimate the speaking rate by segmenting the speech into syllable-like units using end point detection algorithms which do not require any training and fine-tuning. Also, there are no predefined constraints on the expected number of syllabic segments. The acoustic subword units are obtained only from speech signal to estimate the speaking rate without any requirement of transcriptions or phonetic knowledge of the speech data. A recent theta-rate oscillator based syllabification algorithm is also employed for speaking rate estimation. The performance is evaluated on TIMIT corpus and spontaneous speech from Switchboard corpus. The correlation results are comparable to recent algorithms which are trained with specific training set and/or make use of the available transcriptions. © 2019 IEEE.

关键词： Point contacts

How phonotactics affect multilingual and zero-shot ASR performance

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arXiv 2020年

作者： Feng, Siyuan Zelasko, Piotr Moro-Velázquez, Laureano Abavisani, Ali Hasegawa-Johnson, Mark Scharenborg, Odette Dehak, Najim Multimedia Computing Group Delft University of Technology Delft Netherlands Center for Language and Speech Processing United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States

The idea of combining multiple languages’ recordings to train a single automatic speech recognition (ASR) model brings the promise of the emergence of universal speech representation. Recently, a Transformer encoder-decoder model has been shown to leverage multilingual data well in IPA transcriptions of languages presented during training. However, the representations it learned were not successful in zero-shot transfer to unseen languages. Because that model lacks an explicit factorization of the acoustic model (AM) and language model (LM), it is unclear to what degree the performance suffered from differences in pronunciation or the mismatch in phonotactics. To gain more insight into the factors limiting zero-shot ASR transfer, we replace the encoder-decoder with a hybrid ASR system consisting of a separate AM and LM. Then, we perform an extensive evaluation of monolingual, multilingual, and crosslingual (zero-shot) acoustic and language models on a set of 13 phonetically diverse languages. We show that the gain from modeling crosslingual phonotactics is limited, and imposing a too strong model can hurt the zero-shot transfer. Furthermore, we find that a multilingual LM hurts a multilingual ASR system’s performance, and retaining only the target language’s phonotactic data in LM training is preferable. Copyright © 2020, The Authors. All rights reserved.

关键词： speech recognition