Estimating intentions to speak using Lexical information

Estimating intentions to speak using Lexical information: Leveraging Lexical Information to Facilitate Social Interactions with Artificial Agents

Yildiz, Ferhan (TU Delft Electrical Engineering, Mathematics and Computer Science)

Hung, H.S. (mentor)
Elnouty, A.W.F.A.M. (graduation committee)

Delft University of Technology

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

This research paper implements, evaluates, and compares two approaches, a machine learning (ML) approach and a rule-based approach, aimed to estimate intentions to speak. The ML approach trains lexical information extracted from time windows surrounding speech events. The rule-based approach looks for specific keywords or utterances to identify intentions to speak. The results show that the ML approach is a more favourable solution to the problem due to its adaptability and potential for improvement. Sample generation and parameter tweaking showed to be vital to the performance of the model, with its best performance being when it predicted unsuccessful intentions to continue speaking. This study concludes that a machine learning approach can be a viable solution for estimating intentions to continue speaking, with there being future use cases in conversational systems and human-computer interactions.

intentions to speak
lexical information
turn taking
estimating intentions to speak

http://resolver.tudelft.nl/uuid:bf1f59f3-3fb6-4734-9eb1-5b98e6a8f6d9

Student theses

bachelor thesis

© 2023 Ferhan Yildiz