Quantifying robustness of the /t/-/k/ contrast using a single, static spectral feature.

Dynamic spectral shape features accurately classify /t/ and /k/ productions across speakers and contexts. This paper shows that word-initial /t/ and /k/ tokens produced by 21 adults can be differentiated using a single, static spectral feature when spectral energy concentration is considered relative to expectations within a given speaker and vowel context. Centroid and peak frequency-calculated from both acoustic and psychoacoustic spectra-were compared to determine whether one feature could reliably differentiate /t/ and /k/, and, if so, which feature best differentiated them. Centroid frequency from both acoustic and psychoacoustic spectra accurately classified productions of /t/ and /k/.

[1]  B. McMurray,et al.  What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations. , 2011, Psychological review.

[2]  Patrick F. Reidy Spectral dynamics of sibilant fricatives are contrastive and language specific. , 2016, The Journal of the Acoustical Society of America.

[3]  T. Jaeger,et al.  Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. , 2008, Journal of memory and language.

[4]  Paul A. Luce,et al.  Time-varying features of initial stop consonants in auditory running spectra: A first report , 1984, Perception & psychophysics.

[5]  S. Blumstein,et al.  Invariant cues for place of articulation in stop consonants. , 1978, The Journal of the Acoustical Society of America.

[6]  Markus Brauer,et al.  Linear Mixed-Effects Models and the Analysis of Nonindependent Data: A Unified Framework to Analyze Categorical and Continuous Independent Variables that Vary Within-Subjects and/or Within-Items , 2017, Psychological methods.

[7]  S. Zahorian,et al.  Dynamic spectral shape features as acoustic correlates for initial stop consonants , 1991 .

[8]  Brian R Glasberg,et al.  Derivation of auditory filter shapes from notched-noise data , 1990, Hearing Research.

[9]  D. Bates,et al.  fitting linear mixed effects models using lme 4 arxiv , 2014 .

[10]  Jeffrey J. Holliday,et al.  Quantifying the Robustness of the English Sibilant Fricative Contrast in Children. , 2015, Journal of speech, language, and hearing research : JSLHR.

[11]  P. Milenkovic,et al.  Statistical analysis of word-initial voiceless obstruents: preliminary data. , 1988, The Journal of the Acoustical Society of America.

[12]  D Kewley-Port,et al.  Time-varying features as correlates of place of articulation in stop consonants. , 1983, The Journal of the Acoustical Society of America.

[13]  S. Blumstein,et al.  Acoustic invariance in speech production: evidence from measurements of the spectral characteristics of stop consonants. , 1979, The Journal of the Acoustical Society of America.