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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...24 Hits 1 – 20 of 470 1 A prospective study of associations between early fearfulness and perceptual sensitivity and later restricted and repetitive behaviours in infants with typical and elevated likelihood of Autism Narvekar, N.; Carter Leno, V.; Pasco, G.. - : Sage, 2022 BASE Show details 2 Semi-supervised cycle-consistency training for end-to-end ASR using unpaired speech Wu, Ningkai. - 2022 Abstract: The thesis is a replication of the work by Takaaki Hori and his colleagues (2019), which introduces a new method to train end-to-end automatic speech recognition (ASR) models using unpaired speech. In general, large amounts of paired data (speech and text) are needed to train an end-to-end automatic speech recognition system. To alleviate the problem of limited paired data, the idea of cycle-consistency losses has been proposed recently in areas such as machine translation and computer vision. In ASR, cycle-consistency training is achieved by building a reverse system, e.g., a text-to-speech system, and designing a loss based on the reconstructed signal and the original one. However, it is not straightforward to apply cycle-consistency in ASR as information would be lost in the text bottleneck. Tomoki Hayashi et al. (2018) tackled this problem via a text-to-encoder (TTE) model, which predicts encoder states extracted by a pre-trained end-to-end ASR encoder from text input. In this work, the TTE model was used as the reverse system and a loss was defined by comparing the original ASR encoder states and the reconstructed encoder states from the TTE model. Using encoder states instead of raw acoustic features as targets, the model can learn attention much faster and avoid the modeling of speaker dependencies. Our experimental results on the LibriSpeech corpus were similar to the results of Hori et al. The initial ASR and TTE models were trained with LibriSpeech 100-hour paired speech data. By applying cycle-consistency loss and retraining the speech-to-text-to-encoder chain model using one third of LibriSpeech 360-hour unpaired speech data, ASR word error rate was reduced from 25.8% to 21.7% on the LibriSpeech 5-hour test data. ; U of I Only ; Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Keyword: Semi-supervised training; Speech recognition URL: http://hdl.handle.net/2142/108196 BASE Hide details 3 Incorporating Temporal Information in Entailment Graph Mining ... Guillou, Liane; de Vroe, Sander Bijl; Hosseini, Mohammad Javad. - : arXiv, 2021 BASE Show details 4 Blindness to Modality Helps Entailment Graph Mining ... Guillou, Liane; de Vroe, Sander Bijl; Johnson, Mark. - : arXiv, 2021 BASE Show details 5 Implementing two-stage consent pathway in neonatal trials Mitchell, Eleanor; Oddie, Sam J; Dorling, Jon. - 2021 BASE Show details 6 Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction ... NAACL 2021 2021; ., Stephen; Johnson, Mark. - : Underline Science Inc., 2021 BASE Show details 7 Investigating the Mechanisms Driving Referent Selection and Retention in Toddlers at Typical and Elevated Likelihood for Autism Spectrum Disorder. ... Gliga, Teodora; Skolnick, Alex; Liersch, Ute. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 8 Implementing two-stage consent pathway in neonatal trials Mitchell, Eleanor; Oddie, Sam J; Dorling, Jon. - 2021 BASE Show details 9 Improving multilingual speech recognition systems Gao, Heting. - 2021 BASE Show details 10 Enforcing constraints for multi-lingual and cross-lingual speech-to-text systems Ni, Junrui. - 2021 BASE Show details 11 Knowledge base integration in biomedical natural language processing applications Sakakini, Tarek. - 2021 BASE Show details 12 Learning speech embeddings for speaker adaptation and speech understanding Sari, Leda. - 2021 BASE Show details 13 Modeling phones, keywords, topics and intents in spoken languages Chen, Wenda. - 2021 BASE Show details 14 Investigating the Mechanisms Driving Referent Selection and Retention in Toddlers at Typical and Elevated Likelihood for Autism Spectrum Disorder. Gliga, Teodora; Skolnick, Alex; Liersch, Ute. - 2021 BASE Show details 15 Infant EEG theta modulation predicts childhood intelligence Jones, Emily J.H.; Goodwin, A.; Orekhova, E.. - : Nature Publishing Group, 2020 BASE Show details 16 Neural and behavioural indices of face processing in siblings of children with autism spectrum disorder (ASD): a longitudinal study from infancy to mid-childhood Shephard, E.; Milosavljevic, Bosiljka; Mason, Luke. - : Elsevier, 2020 BASE Show details 17 Speech technology for unwritten languages Scharenborg, Odette; Besacier, Laurent; Black, Alan... In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.inria.fr/hal-02480675 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩ (2020) BASE Show details 18 Incorporating Temporal Information in Entailment Graph Mining ... The 28th International Conference on Computational Linguistics 2020; Bijl de Vroe, Sander; Guillou, Liane. - : Underline Science Inc., 2020 BASE Show details 19 How Phonotactics Affect Multilingual and Zero-shot ASR Performance ... Feng, Siyuan; Żelasko, Piotr; Moro-Velázquez, Laureano. - : arXiv, 2020 BASE Show details 20 That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages ... Żelasko, Piotr; Moro-Velázquez, Laureano; Hasegawa-Johnson, Mark. - : arXiv, 2020 BASE Show details Page: 1 2 3 4 5...24

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