This paper introduces DASS2019_NLP, a newly cleaned and curated version of the Digital Archive of Southern Speech, a major historical resource for the study of Southern American English, together with six Whisper ASR models fine-tuned on the data. The 344 hours of conversational speech were recorded by fieldworkers between 1969 and 1983 across the Southern United States. Each Whisper model was fine-tuned on DASS2019_NLP, then evaluated on held-out DASS2019_NLP data, a subset of the Corpus of Regional African American Language (CORAAL), and a subset of Common Voice. The fine-tuned models show consistent learning trajectories and achieve an average 37% reduction in WER on in-domain data relative to baseline models. Notably, they also improve transcription accuracy on CORAAL, suggesting enhanced robustness to African American English. As expected under read vs. conversational style mismatch, accuracy on CV generally favors the OpenAI baselines. Both the DASS2019_NLP dataset and the best-performing fine-tuned model (whisper-large-v3-DASS-ct2) have been publicly released. These resources provide new tools for quantitative research in historical sociolinguistics, facilitating large-scale analyses of phonological, lexical, and grammatical change in Southern and African American English.

Prelateral merger of /e/ and /æ/ is a salient acoustic feature of speech from Melbourne and the state of Victoria in Australia, but little is known about its presence in other parts of the country. In this study, automated methods of data collection, forced alignment, and formant extraction are used to analyze the regional distribution of the vowel merger within all of Australia, in 4.3 million vowel tokens from naturalistic speech in 252 locations. The extent of the merger is quantified using the difference in Bhattacharyya’s distance scores based on phonetic context, and the regional distribution is assessed using spatial autocorrelation. The principal findings are that the merger is most prominent in Victoria and least prominent in Sydney and New South Wales. We also find preliminary indications that it may be present in other parts of the country.

CoANZSE Audio is a searchable online version of the Corpus of Australian and New Zealand Spoken English, a 195-million-word collection of geo-located YouTube transcripts of local government channels. In addition to the part-of-speech-tagged and lemmatized transcript data, CoANZSE Audio provides access to almost all of the underlying audio, as well as to forced alignments of the audio with transcript content, in Praat’s TextGrid format. This paper describes the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. Two possible linguistic analyses based on CoANZSE Audio data are described: use of double modals, a rare syntactic feature, and raising of the mid front vowel /ɛ/ in New Zealand English. CoANZSE Audio can be considered to be among the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes.