Prediction during Processing of Repairs in Spoken Language

University Of California-Davis, Davis CA

Investigators

Abstract

Everyone is familiar with the experience of being disfluent. Despite their best efforts, on average speakers will produce a filler such as "uh" or an "um" every twenty words, and they may also make a speech error which will need to be repaired. Previous research has established some of the causes of disfluency and has revealed that disfluencies of different types characterize different types of speakers; for example, individuals with ADHD are more likely to produce speech errors than to use fillers, and those without ADHD show the opposite pattern. Far less is known, however, about how listeners are able to understand speakers despite the presence of this noise in the linguistic signal. Early proposals tested the hypothesis that listeners must somehow ignore disfluencies, but more recent studies show that disfluencies are only partially suppressed, indicating that disfluencies affect how listeners interpret the sentence they hear and even how they evaluate the speaker. In addition, these newer experiments show that when listeners hear a word spoken in error, they use the error to predict what the speaker is likely to say instead. This prediction mechanism is helpful for two reasons: first, because it allows the listener to get a head start on processing the speaker's intended meaning; and second, because it helps the listener come up with a more sensible interpretation of the utterance should the speaker fail to detect and correct his or her error. Understanding how these prediction mechanisms operate is especially relevant for our understanding of language and aging; speakers are known to become more disfluent as they age, making their speech harder to understand. This is a pressing concern given the aging population of the United States. This work will also help enhance speech recognition devices that must be robust to disfluency if they are to operate on natural, spontaneous speech. Devices that respond to voice commands are now in millions of Americans' homes and pockets, and as they become more common, users will increasingly come to expect them to work smoothly and reliably. The experiments that will be conducted for this project use two complementary methods for assessing people's comprehension of speech on a millisecond-by-millisecond basis: recording of brain electrophysiological activity (EEG), and recording of eye movements to visual displays presented during listening tasks. The experiments are designed to answer three core questions about prediction during processing of disfluencies: (1) When do listeners begin to predict? (2) What precisely is the content of the prediction (a specific word, a general category)? (3) What is the fate of an incorrect prediction? That is, given that listeners' expectations will not always match the speaker's output, how do listeners reconcile their prediction with any discrepant content? This project will involve students who will be trained in experimental psycholinguistics, statistics, and computational methods, allowing them to gain experience in designing and interpreting data, as well as in preparing scientific reports for presentation and publication.