{"_id":"11921","citation":{"ama":"Walter O, Haeb-Umbach R, Chaudhuri S, Raj B. Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling. In: <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>. ; 2013.","bibtex":"@inproceedings{Walter_Haeb-Umbach_Chaudhuri_Raj_2013, title={Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling}, booktitle={IEEE International Conference on Robotics and Automation (ICRA 2013)}, author={Walter, Oliver and Haeb-Umbach, Reinhold and Chaudhuri, Sourish and Raj, Bhiksha}, year={2013} }","ieee":"O. Walter, R. Haeb-Umbach, S. Chaudhuri, and B. Raj, “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling,” in <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013.","mla":"Walter, Oliver, et al. “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.” <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013.","apa":"Walter, O., Haeb-Umbach, R., Chaudhuri, S., &#38; Raj, B. (2013). Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling. In <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>.","chicago":"Walter, Oliver, Reinhold Haeb-Umbach, Sourish Chaudhuri, and Bhiksha Raj. “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.” In <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>, 2013.","short":"O. Walter, R. Haeb-Umbach, S. Chaudhuri, B. Raj, in: IEEE International Conference on Robotics and Automation (ICRA 2013), 2013."},"title":"Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling","year":"2013","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013.pdf","open_access":"1"}],"publication":"IEEE International Conference on Robotics and Automation (ICRA 2013)","user_id":"44006","status":"public","author":[{"first_name":"Oliver","last_name":"Walter","full_name":"Walter, Oliver"},{"id":"242","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"},{"last_name":"Chaudhuri","full_name":"Chaudhuri, Sourish","first_name":"Sourish"},{"first_name":"Bhiksha","full_name":"Raj, Bhiksha","last_name":"Raj"}],"oa":"1","department":[{"_id":"54"}],"abstract":[{"text":"In this paper we consider the unsupervised word discovery from phonetic input. We employ a word segmentation algorithm which simultaneously develops a lexicon, i.e., the transcription of a word in terms of a phone sequence, learns a n-gram language model describing word and word sequence probabilities, and carries out the segmentation itself. The underlying statistical model is that of a Pitman-Yor process, a concept known from Bayesian non-parametrics, which allows for an a priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor processes, language models of different order can be employed and nesting it with another hierarchy of Pitman-Yor processes on the phone level allows for backing off unknown word unigrams by phone m-grams. We present results on a large-vocabulary task, assuming an error-free phone sequence is given. We finish by discussing options how to cope with noisy phone sequences.","lang":"eng"}],"language":[{"iso":"eng"}],"date_created":"2019-07-12T05:30:50Z","date_updated":"2022-01-06T06:51:12Z","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Poster.pdf","description":"Poster","relation":"supplementary_material"},{"relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Spotlight.pdf","description":"Spotlight"}]},"type":"conference"}