Role of Synthetically Generated Samples on Speech Recognition in a Resource-Scarce Language

Speech recognition systems that make use of statistical classifiers require a large number of training samples. However, collection of real samples has always been a difficult problem due to the involvement of substantial amount of human intervention and cost. Considering this problem, this paper presents a novel method for generating synthetic samples from a handful of real samples and investigates the role of these samples in designing a speech recognition system. Speaker dependent limited vocabulary isolated word recognition in an Indian language (i.e. Bengali) has been taken a reference to demonstrate the potential of the proposed framework. The role of synthetic samples is demonstrated by showing a significant improvement in recognition accuracy. A maximum improvement of 10% is achieved using the proposed approach.