Spectral and Temporal Features as Estimators of the Irrelevant Speech Effect

Regular paper

Toros Ufuk Senan

Philips Research Laboratories, NL

Wednesday 3 june, 2015, 09:40 - 10:00

0.6 Madrid (49)

The distractive effects on cognitive processes ascribed to the nature of sound have been studied in the paradigm of “irrelevant sound,” where test participants perform cognitive tasks in the presence of background noise. By comparing the test scores for different acoustic stimulus conditions in such experiments, the “irrelevant sound (speech) effect” (ISE) can be quantified. The ISE is often explained by the changing state hypothesis: the distinctive segmentation of sound tokens; where tokens may be understood as sound segments that can be distinguished from each other in temporal and/or spectral characteristics. A sequence of sounds consisting of differing tokens produces much more disruption than a steady-state sound. The present work investigates the relationship between the features from both temporal and spectral domains and the ISE, predicting separately the magnitude of the effect with two estimators: The Average Modulation Transfer Function (AMTF) and the Frequency Domain Correlation Coefficient (FDCC). The first parameter is a measure for temporal variations in a sound, whilst the latter measures spectral variability in the sounds. Background stimuli are synthesized from a pulse train in which modified and unmodified pulses alternate. In order to manipulate the temporal and spectral features in the stimuli, a numerical optimization method was used to generate two sets of background stimuli where one of the two descriptors was always kept constant and the other was varied in a systematic way. Therefore, stimulus sets used in this study allow the separate estimation of the role of the two estimators on cognitive performance in tasks involving serial ordering of short-term memory content.

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