Multi-Accent Speech Recognition of Afrikaans, Black and White Varieties of South African English

In this paper we investigate speech recognition performance of systems employing several accent-specific recognisers in parallel for the simultaneous recognition of multiple accents. We compare these systems with oracle systems, in which test utterances are presented to matching accent-specific recognisers, and with accent-independent systems, in which acoustic and language model training data are pooled. Our investigation is based on Afrikaans (AE), Black (BE) and White (EE) accents of South African English. We find that, when accent is classified on a per-utterance basis, parallel systems outperform oracle systems for the AE+EE accent pair while the opposite is observed for BE+EE. When accent identification is carried out on a per-speaker basis, oracle or better performance is obtained for both accent pairs. Furthermore, parallel systems based on multi-accent acoustic modelling, which allows selective crossaccent sharing of acoustic training data, outperform parallel systems using accent-specific acoustic models. The former also yields better performance than accent-independent recognition, which uses pooled acoustic and language models.