Improving the Lwazi ASR baseline

We investigate the impact of recent advances in speech recognition techniques for under-resourced languages. Specifically, we review earlier results published on the Lwazi ASR corpus of South African languages, and experiment with additional acoustic modeling approaches. We demonstrate large gains by applying current state-of-the-art techniques, even if the data itself is neither extended nor improved. We analyze the various performance improvements observed, report on comparative performance per technique – across all eleven languages in the corpus – and discuss the implications of our findings for under-resourced languages in general.We would like to thank Brno University of Technology (BUT) and our gracious hosts – Jan (Honza) Cˇ ernocky´, Martin Karafi´at, Karel Versel´y and team – for support during our visit to BUT, and for access to the BUT computing environment where most of these experiments were conducted