Training and testing the TDNN-OPGRU acoustic model on English read and spontaneous speech

Bachelor Thesis (2021)
Author(s)

G.D. Genkov (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

S. Feng – Mentor (TU Delft - Multimedia Computing)

O.E. (Odette) Scharenborg – Graduation committee member (TU Delft - Multimedia Computing)

Catholijn Jonker – Coach (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Georgi Genkov
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Georgi Genkov
Graduation Date
01-07-2021
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

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RP_1_.pdf
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