@inproceedings{rallabandi2018automatic,
author = {Rallabandi, SaiKrishna and Sitaram, Sunayana and Black, Alan W},
title = {Automatic Detection of Code-switching Style from Acoustics},
booktitle = {Workshop on Computational Approaches to Linguistic Code Switching, 2018},
year = {2018},
month = {July},
abstract = {Multilingual speakers switch between languages 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 SpanishEnglish and Hindi-English corpora using two metrics and show that features extracted from acoustics alone can distinguish between different kinds of codeswitching in these language pairs.},
url = {http://approjects.co.za/?big=en-us/research/publication/automatic-detection-code-switching-style-acoustics/},
}