Automatic Detection of Code-switching Style from Acoustics - Rallabandi, SaiKrishna; Sitaram, Sunayana; Black, Alan W (Workshop on Computational Approaches to Linguistic Code Switching, 2018, 2018)
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.