The mean matters: Effects of statistically defined nonspeech spectral distributions on speech categorization

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Abstract
Adjacent speech, and even nonspeech, contexts influence phonetic categorization. Four experiments investigated how preceding sequences of sine-wave tones influence phonetic categorization. This experimental paradigm provides a means of investigating the statistical regularities of acoustic events that influence online speech categorization and, reciprocally, reveals regularities of the sound environment tracked by auditory processing. The tones comprising the sequences were drawn from distributions sampling different acoustic frequencies. Results indicate that whereas the mean of the distributions predicts contrastive shifts in speech categorization, variability of the distributions has little effect. Moreover, speech categorization is influenced by the global mean of the tone sequence, without significant influence of local statistical regularities within the tone sequence. Further arguing that the effect is strongly related to the average spectrum of the sequence, notched noise spectral complements of the tone sequences produce a complementary effect on speech categorization. Lastly, these effects are modulated by the number of tones in the acoustic history and the overall duration of the sequence, but not by the density with which the distribution defining the sequence is sampled. Results are discussed in light of stimulus-specific adaptation to statistical regularity in the acoustic input and a speculative link to talker normalization is postulated.