Samvit Dammalapati


Effects of Duration, Locality, and Surprisal in Speech Disfluency Prediction in English Spontaneous Speech
Samvit Dammalapati | Rajakrishnan Rajkumar | Sidharth Ranjan | Sumeet Agarwal
Proceedings of the Society for Computation in Linguistics 2021


Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech
Samvit Dammalapati | Rajakrishnan Rajkumar | Sumeet Agarwal
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

This study examines the role of three influential theories of language processing, viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.