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Decoding child speech in silence and noise: The type of background noise shapes adults’ processing

  • SUNY Buffalo

Research output: Contribution to journalArticlepeer-review

Abstract

Processing speech that is non-canonical (i.e., child-produced speech) and/or presented in background noise can pose challenges for listeners. We investigated how listening to child-produced speech affects young adults’ word recognition under varying noise conditions. Participants (n = 121) completed a two-picture eye-tracking task in one of three conditions: no background noise, pink background noise, and real-world background noise from LENA recordings. Participants heard a child or adult (Speaker-Age) direct attention to a generic (e.g., keys) or child-specific (e.g., potty; Item-Type) item. We examined the effect of Speaker-Age and Item-Type on participants’ looking time. In no background noise, increases in target looking were high, with greater increases when adults produced generic items. Both pink noise and real-world noise increased task difficulty, but patterns of results varied as a function of speaker gender. For female speech, background noise resulted in an effect of Speaker-Age, with participants increasing their looking time more for adult relative to child speech. The type of background noise did not influence this pattern. For male speech, there was an effect of Speaker-Age in the opposite direction, with participants increasing their looking time more for child relative to adult speech. For male speech, real-world background noise resulted in higher increases in target looking for child-specific items. Together, results suggest that child-produced speech may be more difficult to process than female-adult produced speech in noise, and that listeners can use background noise to predict who will speak and what they might speak about under more challenging conditions, such as processing male speech.

Original languageEnglish
Article number30
JournalAttention, Perception, and Psychophysics
Volume88
Issue number1
DOIs
StatePublished - Jan 2026

Keywords

  • Background noise
  • Child speech
  • Eye-tracking
  • Word prediction
  • Word recognition

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