Speaking Clearly Improves Speech Segmentation By Statistical Learning Under Optimal Listening Conditions

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.