Human communication is typically robust even at high speeds. This suggests that both speakers and listeners efficiently deal with the uncertainty and noise inherent to perception, production, and the environment. This CAREER award investigates how the human brain accomplishes this. A mathematical model of efficient communication based on probability and information theory (the Ideal Speaker model) is tested against data from conversational speech. Specifically, the project investigates how the pronunciation of words in spontaneous speech depends on words' expected confusability in context, the cognitive load the speaker is under and the situational incentive for robust communication. The Ideal Speaker model also predicts that efficient communication with a particular interlocutor requires adaptation to that interlocutor, a prediction that the project tests in behavioral paradigms against task-oriented speech production.
The project contributes to our understanding of how humans produce language, why language has the properties it has, and to what extent the neural systems underlying language production can adjust to different communicative task demands. These insights can contribute to the development of better automatic speech recognition systems (this project is limited to the evaluation of such systems). In addition, novel paradigms to gather large amounts of language data are developed that will dramatically cut research costs. Finally, training in the emerging field of computational psycholinguistics is provided to a broad international audience via summer schools and workshops. This will contribute to a new generation of multidisciplinary scientists working across traditional boundaries between computer science, linguistics, and cognitive psychology.
