Abstract
Multilingual speakers switch between languages in an non-trivial fashion displaying inter sentential, intra sentential, and congruent lexicalization based transitions. While monolingual ASR systems may be capable of recognizing a few words from a foreign language, they are usually not robust enough to handle these varied styles of code-switching. There is also a lack of large code-switched speech corpora capturing all these styles making it difficult to build code-switched speech recognition systems. We hypothesize that it may be useful for an ASR system to be able to first detect the switching style of a particular utterance from acoustics, and then use specialized language models or other adaptation techniques for decoding the speech. In this paper, we look at the first problem of detecting code-switching style from acoustics. We classify code-switched Spanish-English and Hindi-English corpora using two metrics and show that features extracted from acoustics alone can distinguish between different kinds of code-switching in these language pairs.- Anthology ID:
- W18-3209
- Volume:
- Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–81
- Language:
- URL:
- https://aclanthology.org/W18-3209
- DOI:
- 10.18653/v1/W18-3209
- Cite (ACL):
- SaiKrishna Rallabandi, Sunayana Sitaram, and Alan W Black. 2018. Automatic Detection of Code-switching Style from Acoustics. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 76–81, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Detection of Code-switching Style from Acoustics (Rallabandi et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-3209.pdf