Abstract

Although there is a large consensus regarding the involvement of specific acoustic cues in speech perception, the precise mechanisms underlying the transformation from continuous acoustical properties into discrete perceptual units remains undetermined. This gap in knowledge is partially due to the lack of a turnkey solution for isolating critical speech cues from natural stimuli. In this paper, we describe a psychoacoustic imaging method known as the Auditory Classification Image technique that allows experimenters to estimate the relative importance of time-frequency regions in categorizing natural speech utterances in noise. Importantly, this technique enables the testing of hypotheses on the listening strategies of participants at the group level. We exemplify this approach by identifying the acoustic cues involved in da/ga categorization with two phonetic contexts, Al- or Ar-. The application of Auditory Classification Images to our group of 16 participants revealed significant critical regions on the second and third formant onsets, as predicted by the literature, as well as an unexpected temporal cue on the first formant. Finally, through a cluster-based nonparametric test, we demonstrate that this method is sufficiently sensitive to detect fine modifications of the classification strategies between different utterances of the same phoneme.

Highlights

  • In speech perception, we unconsciously process a continuous auditory stream with a complex time-frequency structure that does not contain fixed, highly reproducible, or evident boundaries between the different perceptual elements that we detect in the stream of speech

  • We developed the Auditory Classification Image (ACI) technique by transposing this experimental and statistical framework to an auditory categorization task between two target speech sounds (/aba/ and /ada/)

  • We aimed to 1) further develop the method and complete a first group study to extend the feasibility of the method to group studies; 2) apply statistical tests permitting the evaluation of statistical significance inside or between classification images and 3) explore the specificity of the ACI to the utterances used as targets

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Summary

Introduction

We unconsciously process a continuous auditory stream with a complex time-frequency structure that does not contain fixed, highly reproducible, or evident boundaries between the different perceptual elements that we detect in the stream of speech. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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