Detecting articulatory compensation in acoustic data through linear regression modeling

Alina Khasanova, Jennifer Cole, Mark Allan Hasegawa-Johnson

Research output: Contribution to journalArticlepeer-review


Examining articulatory compensation has been important in understanding how the speech production system is organized, and how it relates to the acoustic and ultimately phonological levels. This paper offers a method that detects articulatory compensation in the acoustic signal, which is based on linear regression modeling of co-variation patterns between acoustic cues. We demonstrate the method on selected acoustic cues for spontaneously produced American English stop consonants. Compensatory patterns of cue variation were observed for voiced stops in some cue pairs, while uniform patterns of cue variation were found for stops as a function of place of articulation or position in the word. Overall, the results suggest that this method can be useful for observing articulatory strategies indirectly from acoustic data and testing hypotheses about the conditions under which articulatory compensation is most likely.

Original languageEnglish (US)
Pages (from-to)925-929
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2014


  • Acoustic cues
  • Articulatory compensation
  • Linear regression modeling

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation


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