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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 Hits 1 – 7 of 7 1 Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech ... Ranjan, Sidharth; Rajkumar, Rajakrishnan; Agarwal, Sumeet. - : University of Massachusetts Amherst, 2022 BASE Show details 2 Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech Ranjan, Sidharth; Rajkumar, Rajakrishnan; Agarwal, Sumeet In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 3 Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones? Bansal, Hritik; Bhatt, Gantavya; Agarwal, Sumeet In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 4 Effects of Duration, Locality, and Surprisal in Speech Disfluency Prediction in English Spontaneous Speech Rajkumar, Rajakrishnan; Agarwal, Sumeet; Dammalapati, Samvit In: Proceedings of the Society for Computation in Linguistics (2021) Abstract: This study examines the role of two influential theories of language processing, Surprisal Theory and Dependency Locality Theory (DLT), in predicting disfluencies (fillers and reparandums) in the Switchboard corpus of English conversational speech. Using Generalized Linear Mixed Models for this task, we incorporate syntactic factors (DLT-inspired costs and syntactic surprisal) in addition to lexical surprisal and duration, thus going beyond the local lexical frequency and predictability used in previous work on modelling word durations in Switchboard speech. Our results indicate that compared to fluent words, words preceding disfluencies tend to have lower lexical surprisal (hence higher activation levels) and lower syntactic complexity (low DLT costs and low syntactic surprisal except for reparandums). Disfluencies tend to occur before upcoming difficulties, i.e., high lexical surprisal words (low activation levels) with high syntactic complexity (high DLT costs and high syntactic surprisal). Further, we see that reparandums behave almost similarly to disfluent fillers with differences possibly arising due to effects being present in the word choice of the reparandum, i.e., in the disfluency itself rather than surrounding it. Moreover, words preceding disfluencies tend to be function words and have longer durations compared to their fluent counterparts, and word duration is a very effective predictor of disfluencies. Overall, speakers may be leveraging the differences in access between content and function words during planning as part of a mechanism to adapt for disfluencies while coordinating between planning and articulation. Keyword: Computational Linguistics; dependency locality theory; disfluencies; psycholinguistics; Psycholinguistics and Neurolinguistics; speech; surprisal URL: https://scholarworks.umass.edu/scil/vol4/iss1/10 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1229&context=scil BASE Hide details 5 Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones? ... Bansal, Hritik; Bhatt, Gantavya; Agarwal, Sumeet. - : arXiv, 2020 BASE Show details 6 How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? ... Bhatt, Gantavya; Bansal, Hritik; Singh, Rishubh. - : arXiv, 2020 BASE Show details 7 Role of Expectation and Working Memory Constraints in Hindi Comprehension: An Eye-tracking Corpus Analysis Agrawal, Arpit; Agarwal, Sumeet; Husain, Samar In: J Eye Mov Res (2017) BASE Show details

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