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

Bachelor thesis (2021)

Authors

G.D. Genkov Electrical Engineering, Mathematics and Computer Science

Contributors

S. Feng (supervisor 1)
O.E. Scharenborg (supervisor 2)
C.M. Jonker Interactive Intelligence - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Georgi Genkov
Phoneme Recognition Spontaneous speech Phoneme Error Rate Acoustic Model TDNN-OPGRU English Prepared speech
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.

Files

RP_1_.pdf
(.pdf | 0.332 Mb)