This study uses a corpus of sociolinguistic interview speech from Western Canadian English to examine individual-level patterns in random forest classification models for fricatives. The models include 23 spectral, durational, and amplitudinal measures commonly used for group-level analyses. Results reveal that measures such as midpoint standard deviation, spectral peak frequency, peak power, and segment duration can capture meaningful distinctions within individual speakers, with some acoustic cues playing a greater role in distinguishing fricatives within speakers than across them. Limiting models to the ten most important predictors slightly increased classification error, suggesting the importance of broader acoustic information.
Kharlamov et al. (Sun,) studied this question.
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