Quantifying Informational Masking due to Masker Intelligibility in Same-talker Speech-in-speech Perception

Mingyue Huo, Yinglun Sun, Daniel Fogerty, Yan Tang

Research output: Contribution to journalConference articlepeer-review

Abstract

Intelligibility of the competing speech plays a significant role in causing informational masking (IM) to the target speech during speech-in-speech perception, especially in same-talker conditions where the target and the masker share a large number of similarities in acoustics. Few studies have quantitatively measured IM as a function of intelligibility of competing speech. Evidence shows that voiced segments are robust cues for speech intelligibility. In this study, the contribution of masker intelligibility to IM was studied by adjusting the voice-to-noise ratio (VNR) on voiced segments of the competing speech, while maintaining energetic masking (EM) at different target-to-masker ratios. Although model estimations suggested that the intelligibility due to EM converged when VNR<0 dB, listener performance showed that more release from IM was received with a further decrease in VNR. It was projected that masker intelligibility could lead to target intelligibility decreased by 50%.

Original languageEnglish (US)
Pages (from-to)1783-1787
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2023-August
DOIs
StatePublished - 2023
Eventthe Annual Conference of the International Speech Communication Association - Dublin, Ireland
Duration: Aug 21 2023Aug 27 2023

Keywords

  • informational masking
  • intelligibility
  • speech-in-speech perception

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Language and Linguistics
  • Human-Computer Interaction
  • Modeling and Simulation

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