The Berkeley Phonetics Machine is a Linux virtual machine imageproduced and used by the UC Berkeley Phonology Lab asa platform for phonetic research. It contains a full data analysisstack based on Python and R and also specialized tools for phoneticresearch. The machine is designed as a flexible and productiveplatform for established and novel research agendas thatcan be easily shared and reproduced. We list the software availablein the machine, which includes many command-line toolsfor acoustic analysis and media file manipulation, as well asspecialized Python libraries. We also discuss the use of this machinein the Phonology Lab and in phonetics courses. The overallexperience with the machine has been positive, as facultyand graduate students are able to share and execute scripts ina common working environment. Undergraduate students haveless opportunity to master the virtual machine environment butbenefit from simplified instructions and fewer installation andoperating problems. The primary difficulty that we have encounteredhas been with a few underpowered student computersthat cannot run the virutual machine or do not run it well.

This is a short paper comparing two approaches to head correction for Electro-MagneticArticulography (EMA) data collected with the Northern Digital Instruments “Wave” system. Inboth of these approaches, it is necessary to translate and rotate the sensor locations to theocclusal coordinate system. We found that point tracking error is greater by as much as doublewith the built-in NDI head correction method, compared to a three-sensor head correctionalgorithm. However, we conclude that the data are comparable, and that the two-sensor NDImethod is acceptable for phonetic research. A Python library for head correction was developedfor this work, and is available on github.com.