Gundluru, R. and Venkatesh, V. and Murty, K S.
(2021)
Attention-based phonetic convolutional recurrent neural networks for language identification.
In: 2021 National Conference on Communications (NCC), 27 July 2021 through 30 July 2021, Virtual, Kanpur.
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Abstract
Language identification is the task of identifying the language of the spoken utterance. Deep neural models such as LSTM-RNN with attention mechanism shown great potential in language identification. The language cues like phonemes and their co-occurrences are an important component while distinguishing the languages. The acoustic feature-based systems do not utilize phonetic information. So the phonetic feature-based LSTM-RNN models have shown improvement over the raw-acoustic features. These methods require a large amount of transcribed speech data to train the phoneme discriminator. Obtaining transcribed speech data for low resource Indian languages is a difficult task. To alleviate this issue, we investigate the usage of pre-trained rich resource phonetic discriminators for low resource target languages to extract the phonetic features. We then trained an attention CRNN based end-to-end utterance level language identification (LID) system with these discriminative phonetic features. We used open-source LibriSpeech English data to train the phoneme discriminator with sequence discriminate objective lattice-free maximum mutual information (LF-MMI). We achieved overall 20% absolute improvements over the baseline acoustic features CRNN model. We also investigate the significance of the duration in LID.
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IITH Creators: |
IITH Creators | ORCiD |
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Murty, K S. | https://orcid.org/0000-0002-6355-5287 |
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Item Type: |
Conference or Workshop Item
(Paper)
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Uncontrolled Keywords: |
Acoustic features, Attention mechanisms, Bottleneck features, Co-occurrence, Feature-based, Language identification, Low resource languages, Neural modelling, Phonetic features, Speech data |
Subjects: |
Electrical Engineering |
Divisions: |
Department of Electrical Engineering |
Depositing User: |
Mrs Haseena VKKM
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Date Deposited: |
15 Nov 2021 06:44 |
Last Modified: |
15 Nov 2021 06:44 |
URI: |
http://raiithold.iith.ac.in/id/eprint/8973 |
Publisher URL: |
https://ieeexplore.ieee.org/document/9530030/ |
Related URLs: |
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