Phone Merging For Code-Switched Speech Recognition

Sunit Sivasankaran, Brij Mohan Lal Srivastava, Sunayana Sitaram, Kalika Bali, Monojit Choudhury


Abstract
Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of code-switched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Anthology ID:
W18-3202
Volume:
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Thamar Solorio, Mona Diab, Julia Hirschberg
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–19
Language:
URL:
https://aclanthology.org/W18-3202
DOI:
10.18653/v1/W18-3202
Bibkey:
Cite (ACL):
Sunit Sivasankaran, Brij Mohan Lal Srivastava, Sunayana Sitaram, Kalika Bali, and Monojit Choudhury. 2018. Phone Merging For Code-Switched Speech Recognition. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 11–19, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Phone Merging For Code-Switched Speech Recognition (Sivasankaran et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-3202.pdf