A Comparison of Transformer and LSTM Encoder Decoder Models for ASR

We present competitive results using a Transformer encoder-decoder-attention model for end-to-end speech recognition needing less training time compared to a similarly performing LSTM model. We observe that the Transformer training is in general more stable compared to the LSTM, although it also see...

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Vydané v:	2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) s. 8 - 15
Hlavní autori:	Zeyer, Albert, Bahar, Parnia, Irie, Kazuki, Schluter, Ralf, Ney, Hermann
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2019
Predmet:	attention Convergence Convolutional codes Data models Decoding Encoding end-to-end ASR Hidden Markov models LSTM Training Transformer
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