The scenarios that will be carried out in this experiment are divided into two scenarios.
The number of parallel corpus used is 1050 sentence lines and the monolingual corpus is 1351 sentence lines. In this study, the data taken came from a book entitled Folklore of Buton and Muna in Southeast Sulawesi and several folklore articles on the internet. The approach used a statistically based using parallel corpus. Therefore, this paper discusses to make a statistical translation machine for the Muna language into Indonesian because at least there are still at least a Muna translation machine into Indonesian. In this rapid technological development, there are still at least some machine translators from regional languages to Indonesian. 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. 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. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms.
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. This study briefly introduces the neural network algorithm for speech recognition.
In order to improve the efficiency of the English translation, machine translation is gradually and widely used.
0 kommentar(er)
