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1 Ergebnisse
1
Syllable-Based Myanmar Speech-to-Text ASR using Deep Learni..:
, In:
2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)
,
Myo, Saw Kyaw Htin
;
Thet, Moe
;
Htun, Hein
... - p. 281-285 , 2024
Link:
https://doi.org/10.1109/ElCon61730.2024.10468151
RT T1 <cTitle>2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)</cTitle>: T1 <Title>Syllable-Based Myanmar Speech-to-Text ASR using Deep Learning and CTC</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10468151&Exemplar=1&LAN=DE A1 Myo, Saw Kyaw Htin A1 Thet, Moe A1 Htun, Hein A1 Myint, Arkar A1 Htut, La Min A1 Shie, Htut YR 2024 SN 2376-6565 K1 Deep learning K1 Training K1 Computational modeling K1 Buildings K1 Linguistics K1 Acoustics K1 Automatic speech recognition K1 Automatic Speech Recognition (ASR) K1 Deep Learning K1 Connectionist Temporal Classification (CTC) K1 Syllable-based Acoustic Model SP 281 OP 285 LK http://dx.doi.org/https://doi.org/10.1109/ElCon61730.2024.10468151 DO https://doi.org/10.1109/ElCon61730.2024.10468151 SF ELIB - SuUB Bremen
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