Assessing agreement level between forced alignment models with data from endangered language documentation corporaAssessing Agreement • January 12th, 2016
Automatic forced alignment between transcriptions has achieved high levels of agreement for languages with large corpora, but the technique holds great promise for work on all languages. Here, we apply two forced alignment programs to data from an endangered Mixtecan language of Mexico. Both yielded a majority of boundaries within 20 ms of hand-labeled ones. Phonemes with fairly steady-state elements (e.g. nasals, fricatives) were more accurately labeled than others. Forced alignment thus may increase efficiency of labeling texts from smaller languages, at least in cases where the phoneme inventories are similar to those of the languages of the training.
Assessing agreement between methods of measurementAssessing Agreement • February 27th, 2017
Assessing agreement on classi cation tasks: the kappa statisticAssessing Agreement • November 7th, 2007
Currently, computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several di erent statistics, none of which are easily interpretable or comparable to each other. Meanwhile, researchers in content analysis have already experienced the same di culties and come up with a solution in the kappa statistic. We discuss what is wrong with reliabil- ity measures as they are currently used for discourse and dialogue work in computational linguistics and cognitive science, and argue that we would be better o as a eld adopting techniques from content analysis.
Assessing agreement between different polygenic risk scores in the UK BiobankAssessing Agreement • July 26th, 2022
Assessing Agreement between Self-Reported and Medical Record Data among Pregnant Latina WomenAssessing Agreement • June 14th, 2005
Authors affiliations: 1 Department of Medicine, University of California, San Francisco, CA. 2 Department of Radiation Oncology, University of California, San Francisco, CA. 3 Department of Pediatrics, University of California, San Francisco, CA. 4 Department of Epidemiology and Biostatistics, University of California, San Francisco, CA. 5 Medical Effectiveness Research Center for Diverse Populations, University of California, San Francisco, CA.
