This paper provides an acoustic phonetic description of Hawai‘i English vowels. The data comprise wordlist tokens produced by twenty-three speakers (twelve males and eleven females) and spontaneous speech tokens produced by ten of those speakers. Analysis of these vowel tokens shows that while there are similarities between Hawai‘i English and other dialects, the particular combination of vowel realizations in Hawai‘i English is unique to this dialect. Additionally, there are characteristics of the Hawai‘i English vowel system that are not found in other English dialects. These findings suggest that Hawai‘i English is a unique regional variety that warrants further description.

In previous work, we have found that New Zealand listeners who produce merged tokens of near and square can accurately distinguish between the vowels in perception even though they report that they are guessing. The ability to distinguish the vowels is affected by a variety of factors for these listeners, including the likelihood that the speaker and experimenter maintain the distinction (Hay et al. 2006b; Hay et al. 2010). In this article, we report on experiments that examine the production and perception of real and nonsense words in the context of two mergers: the Ellen/Allan merger in New Zealand English and the lot/thought merger found in American English. The results demonstrate that speakers’ degree of merger depends at least partially on whether the word is a real or nonsense word. Additionally, the results indicate that a token's real word status affects the merger differently in production and perception. We argue that these results provide evidence in favour of a hybrid model of speech production and perception, one with both abstract phoneme-level representations and acoustically detailed episodic representations.

An increasing number of sociolinguists are using mixed effects models, models which allow for the inclusion of both fixed and random predicting variables. In most analyses, random effect intercepts are treated as a by-product of the model; they are viewed simply as a way to fit a more accurate model. This paper presents additional uses for random effect intercepts within the context of two case studies. Specifically, this paper demonstrates how random intercepts can be exploited to assist studies of speaker style and identity and to normalize for vocal tract size within certain linguistic environments. We argue that, in addition to adopting mixed effect modeling more generally, sociolinguists should view random intercepts as a potential tool during analysis.