Strategies for using MLP based features with limited target-language training data

Abstract

Recently there has been some interest in the question of how to build LVCSR systems when there is only a limited amount of acoustic training data in the target language, but possibly more plentiful data in other languages. In this paper we investigate approaches using MLP based features. We experiment with two approaches: One is based on Automatic Speech Attribute Transcription (ASAT), in which we train classifiers to learn articulatory features. The other approach uses only the target-language data and relies on combination of multiple MLPs trained on different subsets. After system combination we get large improvements of more than 10% relative versus a conventional baseline. These feature-level approaches may also be combined with other, model-level methods for the multilingual or low-resource scenario.