Abstract
Automatic language identification (LID) belongs to the automatic process whereby the identity of the language spoken in a speech sample can be distinguished. In recent decades, LID has made significant advancement in spoken language identification which received an advantage from technological achievements in related areas, such as signal processing, pattern recognition, machine learning and neural networks. This work investigates the employment of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification. The main reason of applying LSTM RNNs to the current task is their reasonable capacity in handling sequences. This study shows that LSTM RNNs can efficiently take advantage of temporal dependencies in acoustic data in order to learn relevant features for language recognition tasks. In this paper, we show results for conducted language identification experiments for Kazakh and Russian languages and the presented LSTM RNN model can deal with short utterances (2s). The model was trained using open-source high-level neural networks API Keras on limited computational resources.
| Original language | English |
|---|---|
| Title of host publication | 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538605011 |
| DOIs | |
| Publication status | Published - Apr 10 2019 |
| Event | 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Moscow, Russian Federation Duration: Sep 20 2017 → Sep 22 2017 |
Publication series
| Name | 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings |
|---|
Conference
| Conference | 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 |
|---|---|
| Country | Russian Federation |
| City | Moscow |
| Period | 9/20/17 → 9/22/17 |
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Keywords
- Language identification
- Long Short-Term Memory Recurrent Neural Networks
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
Kazakh and Russian languages identification using long short-term memory recurrent neural networks. / Kozhirbayev, Zhanibek; Yessenbayev, Zhandos; Karabalayeva, Muslima.
11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8687095 (11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Kazakh and Russian languages identification using long short-term memory recurrent neural networks
AU - Kozhirbayev, Zhanibek
AU - Yessenbayev, Zhandos
AU - Karabalayeva, Muslima
PY - 2019/4/10
Y1 - 2019/4/10
N2 - Automatic language identification (LID) belongs to the automatic process whereby the identity of the language spoken in a speech sample can be distinguished. In recent decades, LID has made significant advancement in spoken language identification which received an advantage from technological achievements in related areas, such as signal processing, pattern recognition, machine learning and neural networks. This work investigates the employment of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification. The main reason of applying LSTM RNNs to the current task is their reasonable capacity in handling sequences. This study shows that LSTM RNNs can efficiently take advantage of temporal dependencies in acoustic data in order to learn relevant features for language recognition tasks. In this paper, we show results for conducted language identification experiments for Kazakh and Russian languages and the presented LSTM RNN model can deal with short utterances (2s). The model was trained using open-source high-level neural networks API Keras on limited computational resources.
AB - Automatic language identification (LID) belongs to the automatic process whereby the identity of the language spoken in a speech sample can be distinguished. In recent decades, LID has made significant advancement in spoken language identification which received an advantage from technological achievements in related areas, such as signal processing, pattern recognition, machine learning and neural networks. This work investigates the employment of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification. The main reason of applying LSTM RNNs to the current task is their reasonable capacity in handling sequences. This study shows that LSTM RNNs can efficiently take advantage of temporal dependencies in acoustic data in order to learn relevant features for language recognition tasks. In this paper, we show results for conducted language identification experiments for Kazakh and Russian languages and the presented LSTM RNN model can deal with short utterances (2s). The model was trained using open-source high-level neural networks API Keras on limited computational resources.
KW - Language identification
KW - Long Short-Term Memory Recurrent Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85052065195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052065195&partnerID=8YFLogxK
U2 - 10.1109/ICAICT.2017.8687095
DO - 10.1109/ICAICT.2017.8687095
M3 - Conference contribution
AN - SCOPUS:85052065195
T3 - 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings
BT - 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -