Abstract
Automatic Speech Recognition (ASR) is reaching further and further into everyday life with Apple’s Siri, Google voice search, automated telephone information systems, dictation devices, closed captioning, and other applications. Along with such advances in speech technology, sociolinguists have been considering new methods for alignment and vowel formant extraction, including techniques like the Penn Aligner (Yuan and Liberman 2008) and the FAVE automated vowel extraction program (Evanini et al. 2009; Rosenfelder et al. 2011). With humans transcribing audio recordings into sentences, these semi-automated methods can produce effective vowel formant measurements (Labov et al. 2013). But as the quality of ASR improves, sociolinguistics may be on the brink of another transformative technology: large-scale, completely automated vowel extraction without any need for human transcription. It would then be possible to quickly extract vowels from virtually limitless hours of recordings, such as YouTube, publicly available audio/video archives, and large-scale personal interviews or streaming video. How far away is this transformative moment? In this article, we introduce a fully automated program called DARLA (short for “Dartmouth Linguistic Automation,” http://darla.dartmouth.edu), which automatically generates transcriptions with ASR and extracts vowels using FAVE. Users simply upload an audio recording of speech, and DARLA produces vowel plots, a table of vowel formants, and probabilities of the phonetic environments for each token. In this paper, we describe DARLA and explore its sociolinguistic applications. We test the system on a dataset of the US Southern Shift and compare the results with semi-automated methods.
Acknowledgements
We are grateful to the anonymous reviewers for their suggestions, and to the various users of DARLA for feedback. Irene Feng assisted in building the web interface. The first author was supported by a Neukom Fellowship at Dartmouth, and development of DARLA is being sponsored by a Neukom CompX grant. The computing cluster used for training the ASR models and running experiments was made available by NSF award CNS-1205521.
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