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  Using HMMs To Attribute Structure To Artificial Languages

Eryilmaz, K., Little, H., & De Boer, B. (2016). Using HMMs To Attribute Structure To Artificial Languages. In S. G. Roberts, C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Feher, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 11th International Conference (EVOLANG11). Retrieved from http://evolang.org/neworleans/papers/125.html.

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EVOLANG_11_paper_125.pdf (Publisher version), 77KB
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 Creators:
Eryilmaz, Kerem1, Author
Little, Hannah1, Author           
De Boer, Bart1, Author
Affiliations:
1Vrije Universiteit Brussel, ou_persistent22              

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 Abstract: We investigated the use of Hidden Markov Models (HMMs) as a way of representing repertoires of continuous signals in order to infer their building blocks. We tested the idea on a dataset from an artificial language experiment. The study demonstrates using HMMs for this purpose is viable, but also that there is a lot of room for refinement such as explicit duration modeling, incorporation of autoregressive elements and relaxing the Markovian assumption, in order to accommodate specific details.

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published online
 Pages: -
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 Rev. Type: Peer
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Title: 11th International Conference on the Evolution of Language (EvoLang XI)
Place of Event: New Orleans
Start-/End Date: 2016-03-20 - 2016-03-24

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Title: The Evolution of Language: Proceedings of the 11th International Conference (EVOLANG11)
Source Genre: Proceedings
 Creator(s):
Roberts, Sean G.1, Editor           
Cuskley, C., Editor
McCrohon, L., Editor
Barceló-Coblijn, L., Editor
Feher, O., Editor
Verhoef, T., Editor
Affiliations:
1 Language and Cognition Department, MPI for Psycholinguistics, Max Planck Society, ou_792548            
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