Basic speech recognition for spoken dialogues

Abstract

Spoken dialogue systems (SDSs) have great potential for information access in the developing world. However, the realisation of that potential requires the solution of several challenging problems, including the development of sufficiently accurate speech recognisers for a diverse multitude of languages. The paper investigates the feasibility of developing small-vocabulary speaker-independent ASR systems designed for use in a telephone-based information system, using ten resource-scarce languages spoken in South Africa as a case study. The researchers contrast a cross-language transfer approach (using a well-trained system from a different language) with the development of new language-specific corpora and systems, and evaluate the effectiveness of both approaches. It was found that limited speech corpora (3 to 8 hours of data from around 200 speakers) are sufficient for the development of reasonably accurate recognisers. Error rates are in the range 2% to 12% for a tenword task, where vocabulary words are excluded from training to simulate vocabulary-independent performance. This approach is substantially more accurate than cross-language transfer, and sufficient for the development of basic spoken dialogue systems.