Efficient data selection for ASR

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. One approach to assist with resource-scarce ASR system development, is to select “useful” training samples which could reduce the resources needed to collect new corpora. In this work, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequencymatched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and N. T. Kleynhans CSIR, Meraka Institute, HLT group & North-West University CSIR Site Building 43 Meiring Naude Road Brummeria Pretoria South Africa Tel.: +27-12-8414264 Fax: +27-12-8414720 E-mail: [email protected] E. Barnard MuST group & North-West University Vaal Triangle Campus Van Eck Blvd Vanderbijlpark 1900 Tel.: +27-16-910-3111 Fax: +27-16-910-3116 E-mail: [email protected] 2 Neil Taylor Kleynhans, Etienne Barnard corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions.