The Lombard intelligibility benefit of native and non-native speech for native and non-native listeners

Katherine Marcoux, Martin Cooke, Benjamin V. Tucker, Mirjam Ernestus

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Speech produced in noise (Lombard speech) is more intelligible than speech produced in quiet (plain speech). Previous research on the Lombard intelligibility benefit focused almost entirely on how native speakers produce and perceive Lombard speech. In this study, we investigate the size of the Lombard intelligibility benefit of both native (American-English) and non-native (native Dutch) English for native and non-native listeners (Dutch and Spanish). We used a glimpsing metric to measure the energetic masking potential of speech, which predicted that both native and non-native Lombard speech could withstand greater amounts of masking to a similar extent, compared to plain speech. In an intelligibility experiment, native English, Spanish, and Dutch listeners listened to the same words, mixed with noise. While the non-native listeners appeared to benefit more from Lombard speech than the native listeners did, each listener group experienced a similar benefit for native and non-native Lombard speech. Energetic masking, as captured by the glimpsing metric, only accounted for part of the Lombard benefit, indicating that the Lombard intelligibility benefit does not only result from a shift in spectral distribution. Despite subtle native language influences on non-native Lombard speech, both native and non-native speech provides a Lombard benefit.

Original languageEnglish (US)
Pages (from-to)53-62
Number of pages10
JournalSpeech Communication
Volume136
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Intelligibility
  • Lombard speech
  • Non-native listeners
  • Non-native speakers

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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