The analysis of human speech perception data often relies on the analysis of overall performance but also on the interpretation of « qualitative » errors based on the exploration of « confusion matrices ». Such confusions are also frequently used in « machine » or « statistical » classification experiments (using either supervised or unsupervised methods, e.g. LDA, logistic regression, clustering…). Within the framework of a project investigating the perception of nasal consonants and vowels in « channel-vocoded » speech and cochlear implanted deaf listeners, I have developed an R package that is devoted to computing « Information Transfer Rate » analyses as described in Miller & Nicely (1955) : a mathematical tool that provides quantitative measurements of qualitative classification errors. Though this approach was introduced as early as the mid-50s by Miller & Nicely (1955) based on Shannon’s (1948) Information Theory and later extended by Wang & Bilger (1973), these tools have recently been reintroduced in the analysis of speech classification tasks (see Christiansen & Greenberg, 2012). I have since started developing an R package (iteR) that is devoted to help users analyse and manipulate confusion matrices within this framework. This package will also be compared with David van Leuwen’s sinfa.R script (https://github.com/davidavdav/sinfa) which is itself based on Wang & Bilger (1973)’s work. Both tools are complementary and iteR may later integrate sinfa.R procedures.
Antje Mefferd (Department of Hearing and Speech Sciences, Vanderbilt University Medical Center)
Jonah Katz (West Virginia University)
Michele Gubian (IPS, LMU Munich)
Nancy C. Kula (University of Essex)