Print Email Facebook Twitter Training and testing the TDNN-OPGRU acoustic model on English read and spontaneous speech Title Training and testing the TDNN-OPGRU acoustic model on English read and spontaneous speech Author Genkov, Georgi (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems) Contributor Feng, S. (mentor) Scharenborg, O.E. (graduation committee) Jonker, C.M. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 Abstract Automatic phoneme recognition (APR) is the process of recognizing phonemes (spoken sounds) in a recording of speech. It can be used for any application requiring fast and accurate transcription, i.e. a courthouse. This research creates such a model using the TDNN-OPGRU architecture and trains it on two datasets of recorded English speech - "TIMIT" for prewritten sentences being read out (prepared/read speech) and "Buckeye" for recorded interviews (spontaneous speech). The results of the model are analyzed and compared to similar research. The main conclusion is that the results obtained do not exceed previous research and in some cases are considerably worse. The reasoning for that is also included. Subject Phoneme RecognitionPhoneme Error RateAcoustic ModelTDNN-OPGRUEnglishPrepared speechSpontaneous speech To reference this document use: http://resolver.tudelft.nl/uuid:350beee7-6bca-41c8-823c-dffd584736eb Part of collection Student theses Document type bachelor thesis Rights © 2021 Georgi Genkov Files PDF RP_1_.pdf 340.48 KB Close viewer /islandora/object/uuid:350beee7-6bca-41c8-823c-dffd584736eb/datastream/OBJ/view