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This paper reports on the role of technology in state-of-the-art pronunciation research and instruction, and makes concrete suggestions for future developments. The point of departure for this contribution is that the goal of second language (L2) pronunciation research and teaching should be enhanced comprehensibility and intelligibility as opposed to native-likeness. Three main areas are covered here. We begin with a presentation of advanced uses of pronunciation technology in research with a special focus on the expertise required to carry out even small-scale investigations. Next, we discuss the nature of data in pronunciation research, pointing to ways in which future work can build on advances in corpus research and crowdsourcing. Finally, we consider how these insights pave the way for researchers and developers working to create research-informed, computer-assisted pronunciation teaching resources. We conclude with predictions for future developments.

This study examines at a new level of quantitative detail the intonation and timing properties of charismatic speech by comparing two popular CEOs, Steve Jobs and Mark Zuckerberg, who are known from informal observations and formal perception experiments alike to be more or less charismatic speakers, respectively. By applying the Fujisaki model we decomposed F0 contours into baseline frequency, phrasal F0 excursions and pitch accent-associated F0 excursions. Timing details are examined by applying Pfitzinger's model of perceived local speech rate to phone and syllable segmentations. Results suggest that high pitch not only involves generally higher F0 levels, but that these increases in F0 are not the same for every prosodic domain or level of the Fujisaki model. In addition we found significant differences depending on whether customers or investors are addressed.

Fundamental frequency (f0) is a highly speaker-specific feature. Consequently, practitioners often use f0 information in forensic casework. Current research principally examines the use of long-term f0 statistics such as f0 means and standard deviations for forensic voice comparison. The present study investigates how short-term f0 features such as measured by the Fujisaki intonation model capture speaker-individuality. Based on data of a homogeneous group of Zurich German speakers, we conducted an experiment on a large corpus of read speech and on a subset of sentences that included speaking style variability (spontaneous vs. read). The latter is characteristic of forensic casework. Speakers demonstrated high between-speaker variability and low within-speaker variability across the two speaking styles for a number of f0 features. Given this evidence of speaker-individuality, we discuss Fujisaki f0 features’ potential for forensic voice comparison.