Design and Development of Spoken Dialogue System in Indic Languages

Shrikant Malviya


Abstract
Based on the modular architecture of a task-oriented Spoken Dialogue System (SDS), the presented work focussed on constructing all the system components as statistical models with parameters learned directly from the data by resolving various language-specific and language-independent challenges. In order to understand the research questions that underlie the SLU and DST module in the perspective of Indic languages (Hindi), we collect a dialogue corpus: Hindi Dialogue Restaurant Search (HDRS) corpus and compare various state-of-the-art SLU and DST models on it. For the dialogue manager (DM), we investigate the deep-learning reinforcement learning (RL) methods, e.g. actor-critic algorithms with experience replay. Next, for the dialogue generation, we incorporated Recurrent Neural Network Language Generation (RNNLG) framework based models. For speech synthesisers as a last component in the dialogue pipeline, we not only train several TTS systems but also propose a quality assessment framework to evaluate them.
Anthology ID:
2021.icon-main.80
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
654–657
Language:
URL:
https://aclanthology.org/2021.icon-main.80
DOI:
Bibkey:
Cite (ACL):
Shrikant Malviya. 2021. Design and Development of Spoken Dialogue System in Indic Languages. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 654–657, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
Design and Development of Spoken Dialogue System in Indic Languages (Malviya, ICON 2021)
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https://aclanthology.org/2021.icon-main.80.pdf