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Effects of Speech Clarity on Recognition Memory for Spoken Sentences

Overview of attention for article published in PLOS ONE, September 2012
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Title
Effects of Speech Clarity on Recognition Memory for Spoken Sentences
Published in
PLOS ONE, September 2012
DOI 10.1371/journal.pone.0043753
Pubmed ID
Authors

Kristin J. Van Engen, Bharath Chandrasekaran, Rajka Smiljanic

Abstract

Extensive research shows that inter-talker variability (i.e., changing the talker) affects recognition memory for speech signals. However, relatively little is known about the consequences of intra-talker variability (i.e. changes in speaking style within a talker) on the encoding of speech signals in memory. It is well established that speakers can modulate the characteristics of their own speech and produce a listener-oriented, intelligibility-enhancing speaking style in response to communication demands (e.g., when speaking to listeners with hearing impairment or non-native speakers of the language). Here we conducted two experiments to examine the role of speaking style variation in spoken language processing. First, we examined the extent to which clear speech provided benefits in challenging listening environments (i.e. speech-in-noise). Second, we compared recognition memory for sentences produced in conversational and clear speaking styles. In both experiments, semantically normal and anomalous sentences were included to investigate the role of higher-level linguistic information in the processing of speaking style variability. The results show that acoustic-phonetic modifications implemented in listener-oriented speech lead to improved speech recognition in challenging listening conditions and, crucially, to a substantial enhancement in recognition memory for sentences.

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Geographical breakdown

Country Count As %
United States 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 26%
Student > Master 9 14%
Researcher 7 11%
Student > Postgraduate 5 8%
Student > Bachelor 5 8%
Other 14 22%
Unknown 8 12%
Readers by discipline Count As %
Linguistics 16 25%
Psychology 13 20%
Engineering 5 8%
Computer Science 4 6%
Medicine and Dentistry 4 6%
Other 10 15%
Unknown 13 20%