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Energetic and Informational Components of Speech-on-Speech Masking in Binaural Speech Intelligibility and Perceived Listening Effort.

Jan Rennies | Virginia Best | Elin Roverud | Gerald Kidd
Trends in hearing | 2019

Speech perception in complex sound fields can greatly benefit from different unmasking cues to segregate the target from interfering voices. This study investigated the role of three unmasking cues (spatial separation, gender differences, and masker time reversal) on speech intelligibility and perceived listening effort in normal-hearing listeners. Speech intelligibility and categorically scaled listening effort were measured for a female target talker masked by two competing talkers with no unmasking cues or one to three unmasking cues. In addition to natural stimuli, all measurements were also conducted with glimpsed speech-which was created by removing the time-frequency tiles of the speech mixture in which the maskers dominated the mixture-to estimate the relative amounts of informational and energetic masking as well as the effort associated with source segregation. The results showed that all unmasking cues as well as glimpsing improved intelligibility and reduced listening effort and that providing more than one cue was beneficial in overcoming informational masking. The reduction in listening effort due to glimpsing corresponded to increases in signal-to-noise ratio of 8 to 18 dB, indicating that a significant amount of listening effort was devoted to segregating the target from the maskers. Furthermore, the benefit in listening effort for all unmasking cues extended well into the range of positive signal-to-noise ratios at which speech intelligibility was at ceiling, suggesting that listening effort is a useful tool for evaluating speech-on-speech masking conditions at typical conversational levels.

Pubmed ID: 31172880

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Associated grants

  • Agency: NIDCD NIH HHS, United States
    Id: R01 DC004545

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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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