Dr Patrycja Strycharczuk
Senior Lecturer in Linguistics & Quantitative Methods @ The University of Manchester
Forced alignment for speech research
This workshop will be an introduction to forced alignment, an automated procedure for phonetic segmentation of speech, which uses orthographic transcription as input. Forced alignment substantially speeds up the processing of sound files for analysis, and in some cases, it can be used to fully automate aspects of phonetic analysis. In the workshop, I will introduce several web-based forced alignment services, and provide hands-on training on how to use them. This approach does not require advanced computer skills. The workshop is intended for researchers who have no experience with forced alignment and who would like to use it for acoustic analysis. It will be introductory and therefore accessible for undergraduate students.
Dr Massimiliano Canzi
Lab Manager / Data Scientist @ Universität Konstanz
Breaking Bad Habits in Experimental and Quantitative Linguistics Research
The goal of this workshop is to share some of the most common mistakes and points of weakness of experimental design in Linguistics I have encountered, as well as in data processing and analysis. Often I am invited to join the conversation at the data analysis stage of a project, where most previous mistakes in experimental design and data collection cannot be fixed anymore. Unfortunately, there is no ideal solution to not-ideal data. You will always have to compromise. By avoiding these mistakes and trying to follow good practices as often as possible, you will learn to collect high-quality data that will serve you much better when it comes to answering your research questions. The level of the workshop is fairly introductory and the workshop is primarily aimed at early-career researchers (PhD and Postdoctoral researchers) and students.
Dr Christopher Carignan
Lecturer in Speech Science @ University College London
Digital Signal Processing in R
In phonetics and speech science research, the R programming environment is commonly used for data wrangling and performing a vast array of statistical analyses. However, given the focus on using the R language for statistical modeling, it is not often used as an environment for primary data analysis. A typical workflow might consist of analyzing data in another language such as MATLAB, Python, or Praat and importing the processed data into R for statistical treatment. In this two-day workshop, you will learn how R can be used as an environment for primary analysis of a variety of data related to speech production, including speech acoustics, articulatory kinematics, and vocal tract imaging. This workshop is designed for participants who have some degree of experience in R and will therefore assume a basic level of knowledge of the R language.
Date: June 8th & 9th, 2-4 pm CET
Where: Konstanz (Online)
Sign Up (for free): Eventbrite
Download Workshop Material: TBD
Dr Stefano Coretta
Senior Teaching Coordinator (Statistics) at the University of Edimburgh
Intro to Bayesian for Speech and Language Scientists
This workshop will introduce Bayesian inference for the quantification of phonetic data using a unified framework of statistical modelling using linear models. Until recently, Bayesian modelling was technically involved and computationally expensive. These challenges have now been overcome, making Bayesian inference conceptually, technically, and computationally feasible for researchers across disciplines. Furthermore, Bayesian inference more directly answers research questions typically asked in the speech sciences, compared to traditional Null Hypothesis Significance Testing, by quantifying magnitude and uncertainty of estimates of interest. A brief conceptual introduction will be followed by a walk through of a Bayesian statistical analysis using R and the package brms (Bürkner 2017). We will explain how to set up a Bayesian regression model (including setting appropriate priors), how to interpret the results inferentially, how to conduct model checks, and how to visualize and report the results. In hands-on exercises, the participants will immediately apply their knowledge to real data sets in R.