The Utilization of Psychometric Functions to Predict Speech
Intelligibility in Vehicles 10-08-01-0002
This also appears in
SAE International Journal of Vehicle Dynamics, Stability, and NVH-V133-10EJ
In this study, a novel assessment approach of in-vehicle speech intelligibility
is presented using psychometric curves. Speech recognition performance scores
were modeled at an individual listener level for a set of speech recognition
data previously collected under a variety of in-vehicle listening scenarios. The
model coupled an objective metric of binaural speech intelligibility (i.e., the
acoustic factors) with a psychometric curve indicating the listener’s speech
recognition efficiency (i.e., the listener factors). In separate analyses, two
objective metrics were used with one designed to capture spatial release from
masking and the other designed to capture binaural loudness. The proposed
approach is in contrast to the traditional approach of relying on the speech
recognition threshold, the speech level at 50% recognition performance averaged
across listeners, as the metric for in-vehicle speech intelligibility. Results
from the presented analyses suggest the importance of considering speech
recognition accuracy across a range of signal-to-noise ratios rather than the
speech recognition threshold alone, and the importance of considering individual
differences among listeners when evaluating in-vehicle speech
intelligibility.
Citation: Samardzic, N., Lavandier, M., and Shen, Y., "The Utilization of Psychometric Functions to Predict Speech Intelligibility in Vehicles," SAE Int. J. Veh. Dyn., Stab., and NVH 8(1):21-30, 2024, https://doi.org/10.4271/10-08-01-0002. Download Citation
Author(s):
Nikolina Samardzic, Mathieu Lavandier, Yi Shen
Affiliated:
Lawrence Technological University, Department of Engineering
Technology, USA, University of Lyon, ENTPE, Ecole Centrale de Lyon, France, University of Washington, Department of Speech and Hearing Sciences,
USA
Pages: 10
ISSN:
2380-2162
e-ISSN:
2380-2170
Related Topics:
Voice / speech
Acoustics
Interior noise
Simulation and modeling
Human factors
Sound quality
Mental processes
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