Why Speech Researchers Should Give Bayesian Statistics a Shot
Show notes
Today's clip is from Episode 168 of Learning Bayesian Statistics, featuring Santiago Barreda, who joins host Alex Andorra to discuss Bayesian multilevel models and their applications in speech perception and linguistics.
Santiago explains how tools like BRMS have made Bayesian modelling much more accessible, lowering the barrier to entry for researchers working in linguistics and speech perception. He also addresses some common concerns around Bayesian methods, particularly the choice of priors, arguing that these concerns are often overstated and that their influence can be examined directly.
He highlights the value of multilevel models in understanding listener variation within speech communities, as well as their flexibility in handling outliers, heteroscedasticity, ordinal data and multinomial data. Alex adds to the discussion by sharing a practical example of fitting mixed-effects models using PyMC, including how different prior choices can affect the results.
Full discussion here
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