Statistical learning of multiple speech streams: A challenge for monolingual infants.

Viridiana L Benitez, Federica Bulgarelli, Krista Byers-Heinlein, Jenny R Saffran, Daniel J Weiss
Author Information
  1. Viridiana L Benitez: Arizona State University, Tempe, AZ, USA.
  2. Federica Bulgarelli: Duke University, Durham, NC, USA.
  3. Krista Byers-Heinlein: Concordia University, Montreal, QC, Canada.
  4. Jenny R Saffran: University of Wisconsin-Madison, Madison, WI, USA.
  5. Daniel J Weiss: Pennsylvania State University, University Park, PA, USA.

Abstract

Language acquisition depends on the ability to detect and track the distributional properties of speech. Successful acquisition also necessitates detecting changes in those properties, which can occur when the learner encounters different speakers, topics, dialects, or languages. When encountering multiple speech streams with different underlying statistics but overlapping features, how do infants keep track of the properties of each speech stream separately? In four experiments, we tested whether 8-month-old monolingual infants (N = 144) can track the underlying statistics of two artificial speech streams that share a portion of their syllables. We first presented each stream individually. We then presented the two speech streams in sequence, without contextual cues signaling the different speech streams, and subsequently added pitch and accent cues to help learners track each stream separately. The results reveal that monolingual infants experience difficulty tracking the statistical regularities in two speech streams presented sequentially, even when provided with contextual cues intended to facilitate separation of the speech streams. We discuss the implications of our findings for understanding how infants learn and separate the input when confronted with multiple statistical structures.

Keywords

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Grants

  1. R01 HD067250/NICHD NIH HHS
  2. R37 HD037466/NICHD NIH HHS
  3. U54 HD090256/NICHD NIH HHS

MeSH Term

Cues
Female
Humans
Infant
Language
Language Development
Learning
Male
Speech
Speech Perception

Word Cloud

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