Speech Disfluencies: Their Role in Comprehension and Word Learning

One of the most extraordinary aspects of human development is how children acquire their language(s) by listening to spontaneous speech. Perhaps more remarkably, they do so even though speech is often highly disfluent. To better understand how language acquisition unfolds, this dissertation explored the effects of speech disfluencies on real-time word comprehension and on word learning for listeners from different language backgrounds and levels of expertise: monolingual children, bilingual children, and bilingual adults. Manuscript 1 reports two word comprehension studies, looking at the ability of children and adults to use disfluencies to predict whether a speaker will name a novel or a familiar object. This ability was investigated by presenting sentences with disfluencies in listeners’ native and non-native language(s). Study 1 tested 32-month-old monolingual and bilingual children, and Study 2 tested bilingual adults. Results from Studies 1 and 2 indicate that listeners looked more at the novel than the familiar object upon hearing a disfluency, irrespective of participants’ language experience, and whether the disfluency was in participants’ native language(s). Importantly, the results suggest that listeners might attend to a speaker’s uncertainty more than the particular realization of the disfluency. Manuscript 2 investigates the impact of speech disfluencies on novel word learning in monolingual and bilingual 32-month-old children. We considered two contrasting possibilities: (1) Disfluencies will facilitate novel word learning, since listeners direct looks to novel objects upon hearing a disfluency, versus (2) disfluencies will hinder novel word learning, since they signal a speaker’s uncertainty about an object’s label. The results indicate that disfluencies may hinder novel word learning: Children did not learn the novel words following disfluencies, nor the novel words following fluent speech. Though somewhat inconclusive as children did not learn words in either case, these results suggest that children’s word learning may be hindered when a speaker is disfluent. Together, the results from the two manuscripts in this dissertation suggest that speech disfluencies are a double-edge sword: they can be helpful for making predictions during real-time comprehension, but could hinder word learning. These findings have important implications for understanding the role of speech disfluencies in language acquisition

Handling disfluencies in spontaneous language models

In automatic speech recognition, a statistical language model (LM) predicts the probability of the next word on the basis of previously recognized words. For recognition of dictated speech this works reasonably well as (1) large amounts of training data are available which allow for a reliable estimation of the LM (2) sentences are typically well-formed. For spontaneous speech however neither condition is fulfilled. First, written transcripts of spontaneous speech are scarce and definitely insufficient for training good statistical LMs. Second, spontaneous speech is riddled with disfluencies which render the prediction context less uniform. Both factors contribute to the poor performance of an automatic speech recognizer on spontaneous input. In this talk, we will present the first results in our research on language modeling for spontaneous speech. More precisely, the choice of LM prediction context for three typical disfluencies was investigated: (1) repetitions e.g. "That is what what I think" (2) hesitations e.g. "That is what uh I think" (3) sentence restarts e.g. "That is what... Yeah I think so" We will show how adjusting the LM prediction context influences the recognition performance and we will interpret these results.Abstractbook 13th meeting of computational linguistics in The Netherlands - CLIN2002, pp. 26, November 29, 2002, Groningen, The Netherlandsstatus: publishe

Handling disfluencies in spontaneous language models

In automatic speech recognition, a stochastic language model (LM) predicts the probability of the next word on the basis of previously recognized words. For the recognition of dictated speech this method works reasonably well since sentences are typically well-formed and reliable estimation of the probabilities is possible on the basis of large amounts of written text material. However, for spontaneous speech the situation is quite different: disfluencies distort the normal flow of sentences and written transcripts of spontaneous speech are too scarce to train good stochastic LMs. Both factors contribute to the poor performance of automatic speech recognizers on spontaneous input. In this paper we investigate how one specific approach to disfluencies in spontaneous language modeling influences recognition performance.Duchateau J., Laureys T., Demuynck K., Wambacq P., ''Handling disfluencies in spontaneous language models'', Computational linguistics in The Netherlands 2002 - selected papers from the thirteenth CLIN meeting. Series : language and computers - studies in practical linguistics, vol. 47, pp. 39-50, Gaustad T. ed., 2003, Editions Rodopi B.V., Amsterdam/New York (13th computational linguistics in The Netherlands meeting - CLIN2002, November 29, 2002, Groningen, The Netherlands).status: publishe