{"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/1995/ICASSP_1995_Haeb1_paper.pdf","open_access":"1"}],"status":"public","date_updated":"2022-01-06T06:51:08Z","citation":{"ama":"Haeb-Umbach R, Beyerlein P, Thelen E. Automatic Transcription of Unknown Words in a Speech Recognition System. In: <i>ICASSP, Detroit</i>. ; 1995.","apa":"Haeb-Umbach, R., Beyerlein, P., &#38; Thelen, E. (1995). Automatic Transcription of Unknown Words in a Speech Recognition System. In <i>ICASSP, Detroit</i>.","bibtex":"@inproceedings{Haeb-Umbach_Beyerlein_Thelen_1995, title={Automatic Transcription of Unknown Words in a Speech Recognition System}, booktitle={ICASSP, Detroit}, author={Haeb-Umbach, Reinhold and Beyerlein, P. and Thelen, E.}, year={1995} }","ieee":"R. Haeb-Umbach, P. Beyerlein, and E. Thelen, “Automatic Transcription of Unknown Words in a Speech Recognition System,” in <i>ICASSP, Detroit</i>, 1995.","chicago":"Haeb-Umbach, Reinhold, P. Beyerlein, and E. Thelen. “Automatic Transcription of Unknown Words in a Speech Recognition System.” In <i>ICASSP, Detroit</i>, 1995.","mla":"Haeb-Umbach, Reinhold, et al. “Automatic Transcription of Unknown Words in a Speech Recognition System.” <i>ICASSP, Detroit</i>, 1995.","short":"R. Haeb-Umbach, P. Beyerlein, E. Thelen, in: ICASSP, Detroit, 1995."},"year":"1995","title":"Automatic Transcription of Unknown Words in a Speech Recognition System","department":[{"_id":"54"}],"author":[{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","id":"242","first_name":"Reinhold"},{"last_name":"Beyerlein","full_name":"Beyerlein, P.","first_name":"P."},{"first_name":"E.","last_name":"Thelen","full_name":"Thelen, E."}],"type":"conference","publication":"ICASSP, Detroit","date_created":"2019-07-12T05:28:15Z","abstract":[{"lang":"eng","text":"We address the problem of automatically finding an acoustic representation (i.e. a transcription) of unknown words as a sequence of subword units, given a few sample utterances of the unknown words, and an inventory of speaker-independent subword units. The problem arises if a user wants to add his own vocabulary to a speaker-independent recognition system simply by speaking the words a few times. Two methods are investigated which are both based on a maximum-likelihood formulation of the problem. The experimental results show that both automatic transcription methods provide a good estimate of the acoustic models of unknown words. The recognition error rates obtained with such models in a speaker-independent recognition task are clearly better than those resulting from separate whole-word models. They are comparable with the performance of transcriptions drawn from a dictionary."}],"user_id":"44006","_id":"11787","oa":"1"}