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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets
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In: ISSN: 1662-5137 ; Frontiers in Systems Neuroscience ; https://hal.archives-ouvertes.fr/hal-03318691 ; Frontiers in Systems Neuroscience, Frontiers, 2021, 15, pp.653975. ⟨10.3389/fnsys.2021.653975⟩ (2021)
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Online activation of L1 Danish orthography enhances spoken word recognition of Swedish
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In: ISSN: 0332-5865 ; Nordic Journal of Linguistics ; https://hal-amu.archives-ouvertes.fr/hal-03283527 ; Nordic Journal of Linguistics, 2021, pp.1-19. ⟨10.1017/S0332586521000056⟩ (2021)
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Hand-gesture recognition based on EMG and event-based camera sensor fusion: a benchmark in neuromorphic computing
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In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.archives-ouvertes.fr/hal-02617084 ; Frontiers in Neuroscience, Frontiers, 2020, pp.36 ; https://www.frontiersin.org/ (2020)
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Neuroplasticity in Visual Word Recognition: An Exploration of Learning-Related Behavioural and Neural Changes ...
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The temporal dynamics of first and second language processing: ERPs to spoken words in Mandarin-English bilinguals
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In: Brain and Mind Institute Researchers' Publications (2020)
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Traitement neuronal des voix et familiarité : entre reconnaissance et identification du locuteur
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The Role of Surface and Underlying Forms When Processing Tonal Alternations in Mandarin Chinese: A Mismatch Negativity Study
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02415176 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02387373 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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Does the prosodic emphasis of sentential context cause deeper lexical-semantic processing?
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In: ISSN: 2327-3798 ; EISSN: 2327-3801 ; Language, Cognition and Neuroscience ; https://hal.univ-lille.fr/hal-01917002 ; Language, Cognition and Neuroscience, Taylor and Francis, 2019, 34, pp.29-42. ⟨10.1080/23273798.2018.1499945⟩ (2019)
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Acoustic event, spoken keyword and emotional outburst detection
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Abstract:
This thesis presents work in research topics of audio detection. It first describes a system for large-scale multi-label acoustic event detection (AED) in YouTube videos. It explores the potential of the state-of-the-art deep learning classifiers for AED, describes both qualitative and quantitative results (Hit@1 is 47.9%) and presents the pre-trained embedding model as a powerful feature extractor to be adapted to new domains with limited data and improve the detection accuracy (Hit@1 is 58.1%). Second, the thesis focuses on the speech acoustic events and the spoken keyword spotting task for speech. It presents a phonetic keyword spotter as a lightweight alternative to full speech recognition (3x faster, with comparable detection rates and that addresses automatic speech recognition problems). It also explores cross-lingual keyword spotting to support low resource languages and finds that the acoustic model is dominant in determining the cross-lingual keyword search performance. Third, the thesis further presents the emotional outburst detection for infant nonspeech acoustic events. It reports on the efforts to manually code child utterances as being of type “laugh,” “cry,” “fuss,” “babble,” and “hiccup” and to develop the algorithms capable of performing the same task automatically.
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Keyword:
audio event detection; convolutional neural network; emotion detection; hidden Markov model; phonetic keywork spotter; speech recognition; spoken keyword detection
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URL: http://hdl.handle.net/2142/105158
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Event Structure In Vision And Language
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In: Publicly Accessible Penn Dissertations (2019)
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Investigating the Electrophysiology of Long-Term Priming in Spoken Word Recognition
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In: ETD Archive (2018)
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Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-Term Memory, Deep Neural Networks
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Qiu, Liang. - : eScholarship, University of California, 2018
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In: Qiu, Liang. (2018). Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-Term Memory, Deep Neural Networks. UCLA: Electrical Engineering 0303. Retrieved from: http://www.escholarship.org/uc/item/1pz29229 (2018)
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Abstract Concepts and Pictures of Real-World Situations Activate One Another.
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In: Psychology Publications (2018)
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