Mapping the spectrotemporal regions influencing perception of French stop consonants in noise

Understanding how speech sounds are decoded into linguistic units has been a central research challenge over the last century. This study follows a reverse-correlation approach to reveal the acoustic cues listeners use to categorize French stop consonants in noise. Compared to previous methods, this approach ensures an unprecedented level of detail with only minimal theoretical assumptions. Thirty-two participants performed a speech-in-noise discrimination task based on natural /aCa/ utterances, with C = /b/, /d/, /g/, /p/, /t/, or /k/. The trial-by-trial analysis of their confusions enabled us to map the spectrotemporal information they relied on for their decisions. In place-of-articulation contrasts, the results confirmed the critical role of formant consonant-vowel transitions, used by all participants, and, to a lesser extent, vowel-consonant transitions and high-frequency release bursts. Similarly, for voicing contrasts, we validated the prominent role of the voicing bar cue, with some participants also using formant transitions and burst cues. This approach revealed that most listeners use a combination of several cues for each task, with significant variability within the participant group. These insights shed new light on decades-old debates regarding the relative importance of cues for phoneme perception and suggest that research on acoustic cues should not overlook individual variability in speech perception.

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Publication Details

Journal
Scientific Reports
Published
2024-11-07
DOI
https://doi.org/10.1038/s41598-024-77634-w
Citations
3
Primary Topic
Phonetics and Phonology Research
Type
article
Field-Weighted Citation Impact
1.52

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article

Mapping the spectrotemporal regions influencing perception of French stop consonants in noise

Maria Giavazzi, Léo Varnet, Géraldine Carranante, Charlotte Cany et al.
3 citations
Scientific Reports
Phonetics and Phonology Research
1.52
article

Mapping the spectrotemporal regions influencing perception of French stop consonants in noise

Maria Giavazzi, Léo Varnet, Géraldine Carranante, Charlotte Cany, Pouria Farri
article en
3 citations

Abstract

Understanding how speech sounds are decoded into linguistic units has been a central research challenge over the last century. This study follows a reverse-correlation approach to reveal the acoustic cues listeners use to categorize French stop consonants in noise. Compared to previous methods, this approach ensures an unprecedented level of detail with only minimal theoretical assumptions. Thirty-two participants performed a speech-in-noise discrimination task based on natural /aCa/ utterances, with C = /b/, /d/, /g/, /p/, /t/, or /k/. The trial-by-trial analysis of their confusions enabled us to map the spectrotemporal information they relied on for their decisions. In place-of-articulation contrasts, the results confirmed the critical role of formant consonant-vowel transitions, used by all participants, and, to a lesser extent, vowel-consonant transitions and high-frequency release bursts. Similarly, for voicing contrasts, we validated the prominent role of the voicing bar cue, with some participants also using formant transitions and burst cues. This approach revealed that most listeners use a combination of several cues for each task, with significant variability within the participant group. These insights shed new light on decades-old debates regarding the relative importance of cues for phoneme perception and suggest that research on acoustic cues should not overlook individual variability in speech perception.

Scientific ReportsVol. 14(1)
Centre National de la Recherche Scientifique (FR), Université Sorbonne Nouvelle (FR), Université Paris Sciences et Lettres (FR), École Normale Supérieure - PSL (FR), Laboratoire de Phonétique et Phonologie (FR), Laboratoire des Systèmes Perceptifs (FR), Laboratoire de Sciences Cognitives et Psycholinguistique (FR), Laboratoire de Physique des Plasmas (FR)
Agence Nationale de la Recherche
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 16%
Phonetics and Phonology Research
1.52
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