Abstract
This study investigated the effect of speaking style on speech segmentation by statistical learning under optimal and adverse listening conditions. Similar to the intelligibility and memory benefits found in previous studies, enhanced acoustic-phonetic cues of the listener-oriented clear speech could improve speech segmentation by statistical learning compared to conversational speech. Yet, it could not be precluded that hyper-articulated clear speech, reported to have less pervasive coarticulation, would result in worse segmentation than conversational speech. We tested these predictions using an artificial language learning paradigm. Listeners who acquired English before age six were played continuous repetitions of the ‘words’ of an artificial language, spoken either clearly or conversationally and presented either in quiet or in noise at a signal-to-noise ratio of +3 or 0 dB SPL. Next, they recognized the artificial words in a two-alternative forced-choice test. Results supported the prediction that clear speech facilitates segmentation by statistical learning more than conversational speech but only in the quiet listening condition. This suggests that listeners can use clear speech acoustic-phonetic enhancements to guide speech processing dependent on domain-general, signal-independent statistical computations. However, there was no clear speech benefit in noise at either signal-to-noise ratio. We discuss possible mechanisms that could explain these results.
Highlights
An important task in understanding spoken language is the segmentation of continuous speech into discrete words
They were made aware of a subsequent test and those assigned to the +3 dB and 0 dB signal-to-noise ratio (SNR) conditions were warned about the noise in the speech streams
As in some artificial language learning studies (e.g., Frank, Goldwater, Griffiths, & Tenenbaum, 2010; Palmer & Mattys, 2016), participants whose accuracy rates were more than two standard deviations (SDs) below the mean of their condition were excluded as outliers
Summary
An important task in understanding spoken language is the segmentation of continuous speech into discrete words. Two syllables that frequently co-occur are likely to be perceived as word-internal, whereas two syllables with a low co-occurrence are interpreted as spanning a word boundary This form of learning may occur without attention to the speech signal (Fernandes, Kolinsky, & Ventura, 2010; Saffran, Newport, Aslin, Tunick, & Barrueco, 1997) and has been observed in infants (Saffran, Aslin, & Newport, 1996; Thiessen & Saffran, 2003, 2007). Such learning occurs in the visual modality as well (Kirkham, Slemmer, & Johnson, 2002; Turk-Browne, Jungé, & Scholl, 2005), suggesting that speech segmentation by statistical learning is rooted in a domain-general learning mechanism. One subsegmental signal-driven cue that listeners rely on is coarticulation, or the articulatory
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Schedule a callMore From: Laboratory Phonology: Journal of the Association for Laboratory Phonology
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