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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...19 Hits 1 – 20 of 368 1 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios Eskander, Ramy. - 2021 BASE Show details 2 Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning Mozafari, Marzieh. - : HAL CCSD, 2021 In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021) BASE Show details 3 Time-locked Cortical Processing of Speech in Complex Environments ... Kulasingham, Joshua Pranjeevan. - : Digital Repository at the University of Maryland, 2021 BASE Show details 4 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ... Eskander, Ramy. - : Columbia University, 2021 BASE Show details 5 Discriminative feature modeling for statistical speech recognition ... Tüske, Zoltán. - : RWTH Aachen University, 2021 BASE Show details 6 Parlez-vous le hate?: Examining topics and hate speech in the alternative social network Parler Ward, Ethan. - : University of Waterloo, 2021 BASE Show details 7 ASR and Human Recognition Errors: Predictability and Lexical Factors Mansfield, Courtney S. - 2021 BASE Show details 8 Marqueurs discursifs de neurodégénérescence liée à la pathologie Alzheimer Slegers, Antoine. - 2021 BASE Show details 9 Time-locked Cortical Processing of Speech in Complex Environments Kulasingham, Joshua Pranjeevan. - 2021 BASE Show details 10 Crowdsourcing linguistic resources for natural non-standardised languages processing ; Myriadisation de ressources linguistiques pour le traitement automatique de langues non standardisées Millour, Alice. - : HAL CCSD, 2020 In: https://hal.archives-ouvertes.fr/tel-03083213 ; Informatique et langage [cs.CL]. Sorbonne Universite, 2020. Français (2020) BASE Show details 11 Parkinson's desease detection by multimodal analysis combining handwriting and speech signals ; Détection de la maladie de Parkinson par analyse multimodale combinant signaux d’écriture et de parole Taleb, Catherine. - : HAL CCSD, 2020 In: https://tel.archives-ouvertes.fr/tel-03594895 ; Signal and Image Processing. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAT039⟩ (2020) BASE Show details 12 Comment parle un robot ? ; Comment parle un robot ?: Les machines à langage dans la science-fiction Landragin, Frédéric. - : HAL CCSD, 2020. : Le Bélial', 2020 In: https://hal.archives-ouvertes.fr/hal-02548113 ; Le Bélial', 2020, Collection Parallaxe, 978-2-84344-965-9 ; https://www.belial.fr/ (2020) BASE Show details 13 The Effects of Prediction and Speech Rate on Lexical Processing ... Cole, Alissa. - : Digital Repository at the University of Maryland, 2020 BASE Show details 14 Analysis of Speech Parameters as Indicators of Engagement in Conversation ELIAS, CHRISTY. - : Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Science, 2020 BASE Show details 15 Uncovering the effects of semantic context on the cortical processing of continuous speech using computational models of language BRODERICK, MICHAEL. - : Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2020 BASE Show details 16 Investigating the Neural Correlates of Speech Processing & Selective Auditory Attention using EEG TEOH, EMILY SIEW. - : Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2020 Abstract: APPROVED ; Speech comprehension is a remarkable human ability. Most normal-hearing people are adept at attending to a speech stream even amidst a noisy multi-talker background and parsing the layers of information it contains in real-time to uncover its meaning. The cortex is thought to be hierarchically organised to perform the latter feat, with higher levels processing increasingly abstract features. Nonetheless, the precise computations it undertakes and how these processes are modulated by attention remain incompletely understood. The development of novel encoding/decoding approaches for relating high temporal resolution neuroimaging modalities to representations of multivariate stimuli like continuous natural speech have enabled researchers to shed some light on this topic. An important recent discovery is that low-frequency, non-invasive EEG/MEG tracks the amplitude envelope of speech (a low-level acoustic measure conveying many cues important for speech comprehension) and that a robust reconstruction of the measure can be acquired from neural data. Moreover, during cocktail party listening tasks, this tracking and the accuracy of reconstruction have been shown to be modulated by attention. These findings have given rise to further research along several lines, including studies employing the framework to investigate how cortex encodes other speech processing stages along the hierarchy and how this encoding is affected by attention, as well as studies developing the idea of exploiting envelope reconstruction as a means of decoding attentional selection for the implementation of smart devices like steerable hearing aids. In this thesis, we employ a particular encoding/decoding approach ? the temporal response function (TRF) ? in conjunction with EEG to address several questions within these subareas. We first tested the efficacy of a state-of-the-art framework for utilising envelope reconstruction to decode auditory attention (O Sullivan et al., 2015) within the context of a cocktail party paradigm with moving talkers and showed that it is robust. This was motivated by the non-stationarity of real-world environments. We then considered if (1) the decoder weights themselves (i.e., the model weights mapping from EEG data to the acoustic envelope) and (2) alpha power might contain unique information that can be leveraged for the decoding of attention. We showed that incorporating a metric based on the consistency of model weights across subjects into the decoding framework yielded an improvement in performance above and beyond using envelope reconstruction alone. We then investigated the neural processing of prosody ? an aspect of spoken language that conveys another layer of meaning on top of linguistic units. In particular, we were interested to explore how prosodic pitch is encoded in low-frequency EEG during listening to continuous natural speech. We mapped two measures of prosodic pitch ? relative pitch and harmonic resolvability ? to concurrently-recorded EEG. These measures were inspired by an ECoG study showing neural tracking of relative pitch during listening to sentences (Tang, Hamilton, & Chang, 2017), and an fMRI study demonstrating that there are cortical regions that respond primarily to resolved harmonics (Norman-Haignere, Kanwisher, & McDermott, 2013). We found that delta-band EEG tracks relative pitch during listening to continuous natural speech, and that this tracking is dissociable from the tracking of other acoustic and phonetic features. Finally, we tested how attention modulates EEG signatures hypothesized to represent processing at the pre-lexical level, as well as the signature of relative pitch found in the previous study. The former inquiry was motivated by the longstanding debates as to whether attention modulation operates at an early or late stage in the speech processing hierarchy, and whether an acoustic-phonetic transformation stage occurs as an intermediate step in mapping the acoustic speech signal to the mental store of words. We found that a phonetic feature representation could uniquely predict neural activity above and beyond other measures. Additionally, this unique predictive power was significantly modulated by attention, unlike that of a representation derived from acoustics that had been suggested to explain away the contribution of phonetic features (Daube et al., 2019). This lends support to the notion of the phonetic feature representation being a distinct and higher-level stage in the hierarchy. We also found attentional modulation of our signature of relative pitch and showed that incorporating the reconstruction accuracy of this representation into the decoding attentional selection framework led to a small improvement in decoding performance for some subjects. Keyword: EEG; Neural Signal Processing; Neural Speech Processing URL: http://hdl.handle.net/2262/92187 https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:TEOHE BASE Hide details 17 Demographic-Aware Natural Language Processing Garimella, Aparna. - 2020 BASE Show details 18 Robust Methods for the Automatic Quantification and Prediction of Affect in Spoken Interactions Aldeneh, Zakaria. - 2020 BASE Show details 19 Neural Correlates of Phonetic and Lexical Processing in Children with and without Speech Sound Disorder Katelyn L Gerwin (8968220). - 2020 BASE Show details 20 Processamento de fala para triagem de distúrbios fonológicos ; Speech processing for screening off phonological disorders Yoshimura, Guilherme Jun. - : Biblioteca Digital de Teses e Dissertações da USP, 2020. : Universidade de São Paulo, 2020. : Instituto de Matemática e Estatística, 2020 BASE Show details Page: 1 2 3 4 5...19

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