Machine translation of English speech: Comparison of multiple algorithms

In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the en...

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Vydané v:Journal of intelligent systems Ročník 31; číslo 1; s. 159 - 167
Hlavní autori: Wu, Yijun, Qin, Yonghong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin De Gruyter 01.01.2022
Walter de Gruyter GmbH
Predmet:
Algorithms
Back propagation networks
Coders
Decoding
English
Errors
long short-term memory
Machine translation
Neural networks
recurrent neural network
Recurrent neural networks
Speech
Speech recognition
Translations
ISSN:2191-026X, 0334-1860, 2191-026X
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Popis
Shrnutí:In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2191-026X
0334-1860
2191-026X
DOI:10.1515/jisys-2022-0005