Capitalising on North American speech resources for the development of a South African English large vocabulary speech recognition system

• We try to develop or improve a South African (SA) speech recogniser with US data.
• The SA domain is under-resourced while the US domain is very well-resourced.
• US pronunciations and language models can feasibly replace SA counterparts.
• US acoustic data used in a SA system results in a large performance penalty.
• US acoustic and language model data slightly improve a SA system by adaptation.

South African English is currently considered an under-resourced variety of English. Extensive speech resources are, however, available for North American (US) English. In this paper we consider the use of these US resources in the development of a South African large vocabulary speech recognition system. Specifically we consider two research questions. Firstly, we determine the performance penalties that are incurred when using US instead of South African language models, pronunciation dictionaries and acoustic models. Secondly, we determine whether US acoustic and language modelling data can be used in addition to the much more limited South African resources to improve speech recognition performance. In the first case we find that using a US pronunciation dictionary or a US language model in a South African system results in fairly small penalties. However, a substantial penalty is incurred when using a US acoustic model. In the second investigation we find that small but consistent improvements over a baseline South African system can be obtained by the additional use of US acoustic data. Larger improvements are obtained when complementing the South African language modelling data with US and/or UK material. We conclude that, when developing resources for an under-resourced variety of English, the compilation of acoustic data should be prioritised, language modelling data has a weaker effect on performance and the pronunciation dictionary the smallest.