What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations.

Most theories of categorization emphasize how continuous perceptual information is mapped to categories. However, equally important are the informational assumptions of a model, the type of information subserving this mapping. This is crucial in speech perception where the signal is variable and context dependent. This study assessed the informational assumptions of several models of speech categorization, in particular, the number of cues that are the basis of categorization and whether these cues represent the input veridically or have undergone compensation. We collected a corpus of 2,880 fricative productions (Jongman, Wayland, & Wong, 2000) spanning many talker and vowel contexts and measured 24 cues for each. A subset was also presented to listeners in an 8AFC phoneme categorization task. We then trained a common classification model based on logistic regression to categorize the fricative from the cue values and manipulated the information in the training set to contrast (a) models based on a small number of invariant cues, (b) models using all cues without compensation, and (c) models in which cues underwent compensation for contextual factors. Compensation was modeled by computing cues relative to expectations (C-CuRE), a new approach to compensation that preserves fine-grained detail in the signal. Only the compensation model achieved a similar accuracy to listeners and showed the same effects of context. Thus, even simple categorization metrics can overcome the variability in speech when sufficient information is available and compensation schemes like C-CuRE are employed.