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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
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Semi-supervised cycle-consistency training for end-to-end ASR using unpaired speech
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Incorporating Temporal Information in Entailment Graph Mining ...
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Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction ...
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Investigating the Mechanisms Driving Referent Selection and Retention in Toddlers at Typical and Elevated Likelihood for Autism Spectrum Disorder. ...
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Enforcing constraints for multi-lingual and cross-lingual speech-to-text systems
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Knowledge base integration in biomedical natural language processing applications
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Learning speech embeddings for speaker adaptation and speech understanding
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Modeling phones, keywords, topics and intents in spoken languages
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Abstract:
Spoken Language Understanding for both rich-resource languages (RRL) and low-resource languages (LRL) is an important research area for academia and the commercial world. In the conversational situations where either the language used in speech is a minority one, or the environment is noisy, barriers will emerge between the communicators. Essentially, people would like to understand the basic components of any language spoken by others who they meet in their daily lives. On the other hand, machines can also be trained to learn the process of modeling the basic language components such as phones, keywords, topics and intents during both human/machine interactions and human/human communications. Eventually, if we can develop a machine assistant for people to understand the basic meaning of any language in speech, we could make the human world much more efficient and harmonious. This thesis addresses the problem with the help of mismatched-crowdsourcing- based distant supervision, linguistic knowledge, and corpus-based transfer learning. First we analyze the usefulness of mismatched transcripts and distinctive features, and then propose phone recognition based on the optimized inference of the phone set in the low-resource language from the clustering of the mismatched transcripts. Subsequently, the keyword discovery from the phone-level results is explored. The topic information collected in the corpus is then used as the additional knowledge for topic classification and further improving phone recognition. Based on the keyword sequence, the intents of the speaker are also eventually obtained. The experimental results show that with the help of data collection design and existing knowledge, we can achieve reasonably good machine language understanding for languages whose phones, keywords, topics, and intents were not learned before. This work will lead to further investigations in the area of spoken language understanding in any language. ; U of I Only ; Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system
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Keyword:
Clustering; Distant Supervision; Low-resource languages; Speech recognition; Spoken language understanding; Spoken term detection; Transfer learning
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URL: http://hdl.handle.net/2142/105767
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Investigating the Mechanisms Driving Referent Selection and Retention in Toddlers at Typical and Elevated Likelihood for Autism Spectrum Disorder.
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Infant EEG theta modulation predicts childhood intelligence
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Neural and behavioural indices of face processing in siblings of children with autism spectrum disorder (ASD): a longitudinal study from infancy to mid-childhood
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Speech technology for unwritten languages
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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)
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Incorporating Temporal Information in Entailment Graph Mining ...
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How Phonotactics Affect Multilingual and Zero-shot ASR Performance ...
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That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages ...
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