Publications

Abstract

Disfluencies, like 'uh', have been shown to help listeners anticipate reference to low-frequency words. The associative account of this 'disfluency bias' proposes that listeners learn to associate disfluency with low-frequency referents based on prior exposure to non-arbitrary disfluency distributions (i.e., greater probability of low-frequency words after disfluencies). However, there is limited evidence for listeners actually tracking disfluency distributions online. The present experiments are the first to show that adult listeners, exposed to a typical or more atypical disfluency distribution (i.e., hearing a talker unexpectedly say uh before high-frequency words), flexibly adjust their predictive strategies to the disfluency distribution at hand (e.g., learn to predict high-frequency referents after disfluency). However, when listeners were presented with the same atypical disfluency distribution but produced by a non-native speaker, no adjustment was observed. This suggests pragmatic inferences can modulate distributional learning, revealing the flexibility of, and constraints on, distributional learning in incremental language comprehension.

Abstract

Listeners are known to track statistical regularities in speech. Yet, which temporal cues
are encoded is unclear. This study tested effects of talker-specific habitual speech rate
and talker-independent average speech rate (heard over a longer period of time) on
the perception of the temporal Dutch vowel contrast /A/-/a:/. First, Experiment 1
replicated that slow local (surrounding) speech contexts induce fewer long /a:/
responses than faster contexts. Experiment 2 tested effects of long-term habitual
speech rate. One high-rate group listened to ambiguous vowels embedded in `neutral'
speech from talker A, intermixed with speech from fast talker B. Another low-rate group
listened to the same `neutral' speech from talker A, but to talker B being slow.
Between-group comparison of the `neutral' trials showed that the high-rate group
demonstrated a lower proportion of /a:/ responses, indicating that talker A's habitual
speech rate sounded slower when B was faster. In Experiment 3, both talkers
produced speech at both rates, removing the different habitual speech rates of talker A
and B, while maintaining the average rate differing between groups. This time no
global rate effect was observed. Taken together, the present experiments show that a
talker's habitual rate is encoded relative to the habitual rate of another talker, carrying
implications for episodic and constraint-based models of speech perception.

Abstract

Speech can be produced at different rates. Listeners take this rate variation into account by normalizing vowel duration for contextual speech rate: An ambiguous Dutch word /m?t/ is perceived as short /mAt/ when embedded in a slow context, but long /ma:t/ in a fast context. Whilst some have argued that this rate normalization involves low-level automatic perceptual processing, there is also evidence that it arises at higher-level cognitive processing stages, such as decision making. Prior research on rate-dependent speech perception has only used explicit recognition tasks to investigate the phenomenon, involving both perceptual processing and decision making. This study tested whether speech rate normalization can be observed without explicit decision making, using a cross-modal repetition priming paradigm. Results show that a fast precursor sentence makes an embedded ambiguous prime (/m?t/) sound (implicitly) more /a:/-like, facilitating lexical access to the long target word "maat" in a (explicit) lexical decision task. This result suggests that rate normalization is automatic, taking place even in the absence of an explicit recognition task. Thus, rate normalization is placed within the realm of everyday spoken conversation, where explicit categorization of ambiguous sounds is rare.

Abstract

Many psycholinguistic models of speech sequence planning make claims about the onset and offset times of planning units, such as words, syllables, and phonemes. These predictions typically go untested, however, since psycholinguists have assumed that the temporal dynamics of the speech signal is a poor index of the temporal dynamics of the underlying speech planning process. This article argues that this problem is tractable, and presents and validates two simple metrics that derive planning unit onset and offset times from the acoustic signal and articulatographic data.