Speech processing requires sensitivity to long-term regularities of the native language yet demands listeners to flexibly adapt to perturbations that arise from talker idiosyncrasies such as nonnative accent. The present experiments investigate whether listeners exhibit dimension-based statistical learning of correlations between acoustic dimensions defining perceptual space for a given speech segment. While engaged in a word recognition task guided by a perceptually unambiguous voice-onset time (VOT) acoustics to signal beer, pier, deer, or tear, listeners were exposed incidentally to an artificial “accent” deviating from English norms in its correlation of the pitch onset of the following vowel (F0) to VOT. Results across four experiments are indicative of rapid, dimension-based statistical learning; reliance on the F0 dimension in word recognition was rapidly down-weighted in response to the perturbation of the correlation between F0 and VOT dimensions. However, listeners did not simply mirror the short-term input statistics. Instead, response patterns were consistent with a lingering influence of sensitivity to the long-term regularities of English. This suggests that the very acoustic dimensions defining perceptual space are not fixed and, rather, are dynamically and rapidly adjusted to the idiosyncrasies of local experience, such as might arise from nonnative-accent, dialect, or dysarthria. The current findings extend demonstrations of “object-based” statistical learning across speech segments to include incidental, online statistical learning of regularities residing within a speech segment.

The ability to integrate and weight information across dimensions is central to perception and is particularly important for speech categorization. The present experiments investigate cue weighting by training participants to categorize sounds drawn from a two-dimensional acoustic space defined by the center frequency ͑CF͒ and modulation frequency ͑MF͒ of frequency-modulated sine waves. These dimensions were psychophysically matched to be equally discriminable and, in the first experiment, were equally informative for accurate categorization. Nevertheless, listeners' category responses reflected a bias for use of CF. This bias remained even when the informativeness of CF was decreased by shifting distributions to create more overlap in CF. A reversal of weighting ͑MF over CF͒ was obtained when distribution variance was increased for CF. These results demonstrate that even when equally informative and discriminable, acoustic cues are not necessarily equally weighted in categorization; listeners exhibit biases when integrating multiple acoustic dimensions. Moreover, changes in weighting strategies can be affected by changes in input distribution parameters. This methodology provides potential insights into acquisition of speech sound categories, particularly second language categories. One implication is that ineffective cue weighting strategies for phonetic categories may be alleviated by manipulating variance of uninformative dimensions in training stimuli.

This chapter focuses on one of the first steps in comprehending spoken language: How do listeners extract the most fundamental linguistic elements-consonants and vowels, or the distinctive features which compose them-from the acoustic signal? We begin by describing three major theoretical perspectives on the perception of speech. Then we review several lines of research that are relevant to distinguishing these perspectives. The research topics surveyed include categorical perception, phonetic context effects, learning of speech and related nonspeech categories, and the relation between speech perception and production. Finally, we describe challenges facing each of the major theoretical perspectives on speech perception.