Multilingual Communication: NMT-Based On-Call Speech Translation for Indian Languages
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1488-9_9
Reference12 articles.
1. Arya L, Agarwal A, Mishra J, Mahadeva Prasanna SR (2022) Analysis of layer-wise training in direct speech to speech translation using BI-LSTM. In: 25th Conference of the oriental COCOSDA international committee for the co-ordination and standardisation of speech databases and assessment techniques (O-COCOSDA). Hanoi, Vietnam, pp 1–6. https://doi.org/10.1109/O-COCOSDA202257103.2022.9997945
2. Duan C et al (2021) Modeling future cost for neural machine translation. IEEE/ACM Trans Audio Speech Language Process 29:770–781. https://doi.org/10.1109/TASLP.2020.3042006
3. Chen K, Wang R, Utiyama M, Sumita E, Zhao T (2019) Neural machine translation with sentence-level topic context. IEEE/ACM Trans Audio Speech Language Process 27(12):1970–1984. https://doi.org/10.1109/TASLP.2019.2937190. Dec.
4. Duan C et al (2021) Modeling future cost for neural machine translation. IEEE/ACM Trans Audio Speech Language Process 29:770–781. https://doi.org/10.1109/TASLP.2020.3042006
5. Zhang C et al (2020) UWSpeech: speech to speech translation for unwritten languages. In: AAAI conference on artificial intelligence
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