Predicting vowel substitution in code-switched speech

The accuracy of automatic speech recognition (ASR) systems typically degrades when encountering code-switched speech. Some of this degradation is due to the unexpected pronunciation effects introduced when languages are mixed. Embedded (foreign) phonemes typically show more variation than phonemes from the matrix language: either approximating the embedded language pronunciation fairly closely, or realised as any of a set of phonemic counterparts from the matrix language. In this paper we describe a technique for predicting the phoneme substitutions that are expected to occur during code-switching, using non-acoustic features only. As case study we consider Sepedi/English code switching and analyse the different realisations of the English schwa. A code-switched speech corpus is used as input and vowel substitutions identified by auto-tagging this corpus based on acoustic characteristics. We first evaluate the accuracy of our auto-tagging process, before determining the predictability of our auto-tagged corpus, using non-acoustic features.