The present thesis compares competing theories of pre-linguistic normalization for the perception of Swedish and English vowels. Specifically, the overall aim is to investigate whether normalization might be key to understanding the mechanisms supporting robust cross-talker perception, and to gain more insights into the specific computations involved. The thesis is based on three articles that employ acoustic analysis, behavioral experiments and computational modeling to address the question of vowel normalization.

Article I uses a novel phonetically annotated database of Swedish vowel recordings, the SwehVd, to provide an updated acoustic description of the Central Swedish vowel system and to evaluate certain claims of cue-to-category mappings introduced by previous work. Replicating previous studies, the results of Article I suggest that F1, F2 and vowel duration are the most important cues to vowel identity in Central Swedish. In addition, the results highlight the importance of formant dynamics for reliable category distinctions. The acoustic characteristics of Article I further constitute the input to the computational modeling presented in Article II.

Article II evaluates 15 competing normalization accounts in terms of how well they predict the intended vowel category of Central Swedish, as represented by the talkers in SwehVd. Specifically, a computational model of vowel perception, a Bayesian ideal observer, is used to assess the predicted consequences of normalization. The results indicate that normalization accounts that assume the learning and storing of talker-specific acoustics (i.e., extrinsic accounts) achieve the best fit against vowel production data. The evaluation against the SwehVd database further contributes to the insight that languages with dense vowel spaces do not necessarily require more complex normalization mechanisms.

Article III evaluates 20 different normalization accounts in how well they predict listeners' categorization behavior in two vowel categorization experiments on US English vowels. Paralleling the results from Article II, the results indicate that more complex extrinsic normalization is needed for robust cross-talker perception. However, it is a computationally minimalist extrinsic account – uniform scaling – that provides the best fit when evaluated against listeners' responses. This would seem to suggest that more complex computations (as in, e.g., Lobanov normalization) are not required for human speech perception.

The thesis aimed for a broad-scale evaluation of competing theories of pre-linguistic normalization, assessing the predictions of different accounts using different types of experiment stimuli, different vowel spaces, and different sets of acoustic cues. This broad-scale evaluation was made possible through the implementation of a holistic and stringent computational framework, for an unbiased comparison of accounts. The main contributions of this thesis include the open-access publication of the framework and the vowel database, to facilitate replication and future studies.