Abstract
When the National Centre for Human Language Technology (NCHLT) Speech corpus was released, it created various opportunities for speech technology development in the 11 official, but critically under-resourced, languages of South Africa. Since then, the substantial improvements in acoustic modeling that deep architectures achieved for well-resourced languages ushered in a new data requirement: their development requires hundreds of hours of speech. A suitable strategy for the enlargement of speech resources for the South African languages is therefore required. The first possibility was to look for data that has already been collected but has not been included in an existing corpus. Additional data was collected during the NCHLT project that was not included in the official corpus: it only contains a curated, but limited subset of the data. In this paper, we first analyze the additional resources that could be harvested from the auxiliary NCHLT data. We also measure the effect of this data on acoustic modeling. The analysis incorporates recent factorized time-delay neural networks (TDNN-F). These models significantly reduce phone error rates for all languages. In addition, data augmentation and cross-corpus validation experiments for a number of the datasets illustrate the utility of the auxiliary NCHLT data.
Highlights
The development of language and speech technology requires substantial amounts of appropriate data
We report results obtained using time delay neural networks (TDNN)-F acoustic models, which have recently been demonstrated to be effective in resource-constrained scenarios [32]
This selection strategy resulted in some improvement given the TDNN-bi-directional LSTMs (BLSTMs) baseline
Summary
The development of language and speech technology requires substantial amounts of appropriate data. Various strategies have been proposed to collect speech and text resources for technology development, for example harvesting existing data like broadcast news and online publications, crowd-sourcing, web crawling, dedicated data collection campaigns, etcetera [7,8,9,10,11,12,13]. Both data types are required for language and speech technology development, and constructing comprehensive text corpora is just as important as creating speech resources. During the Lwazi project, telephone speech was collected (between four and ten hours per language [2]), while the aim of the first NCHLT project was to collect 50–60 h of orthographically-transcribed, broadband speech in each of the country’s 11 official languages [4]
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