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Speech Intelligibility in Reverberation is Reduced During Self-Rotation
Ľuboš Hládek1, Bernhard U Seeber1
1Audio Information Processing, Technical University of Munich, Munich, Germany.
Trends in Hearing
|July 21, 2023
Summary
People naturally rotate to improve speech understanding in noisy environments. Visual cues enhance self-rotation accuracy and speech intelligibility, though overall intelligibility slightly decreases during self-rotation.
Area of Science:
- Auditory Perception
- Human Behavior
- Acoustic Signal Processing
Background:
- Traditional studies of speech intelligibility in noisy environments assume stationary participants and sound sources.
- Active self-rotation by participants introduces dynamic spatial considerations.
- Understanding adaptive behaviors in complex auditory scenes is crucial.
Purpose of the Study:
- To investigate if individuals spontaneously rotate to optimize speech intelligibility in a spatialized auditory environment.
- To determine the benefit of visual cues (avatar) in guiding self-rotation and improving speech perception.
- To compare speech intelligibility during self-rotation versus a static condition.
Main Methods:
- Participants performed a spatialized speech test involving self-rotation towards auditory targets.
- Speech stimuli were presented from different locations (0°, ±90°, 180°) with frontal noise (0°).
- Conditions included audio-only, audio-visual (with avatar), and a static baseline.
Main Results:
- Participant self-orientations undershot target locations but were near acoustically optimal.
- Speech intelligibility was higher in the audio-visual condition compared to audio-only for lateral targets.
- Intelligibility improved for rear targets during self-rotation but decreased for lateral targets compared to static, partly due to spatial unmasking.
Conclusions:
- Speech intelligibility is generally reduced during self-rotation compared to static conditions.
- Visual location cues facilitate more accurate self-rotations and enhance speech intelligibility.
- Current models of static spatial unmasking may overestimate performance in dynamic self-rotation scenarios.
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