Detecting categorical perception in continuous discrimination data

We present a method for assessing categorical perception from continuous discrimination data. Until recently, categorical perception of speech has exclusively been measured by discrimination and identification experiments with a small number of repeatedly presented stimuli. Experiments by Rogers and Davis [1] have shown that using non-repeating stimuli along a densely-sampled phonetic continuum yields a more reliable measure of categorization. However, no analysis method has been proposed that would preserve the continuous nature of the obtained discrimination data. In the present study, we describe a method of analysis that can be applied to continuous discrimination data without having to discretize the raw data at any time during the analysis.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Paul Boersma, Katerina Chládková
• Keywords: Categorical Perception, Continuous Discrimination Data, Discrimination Data, INTERSPEECH 2010, Signal Processing