The interest in this paper is in efficient configuration of automatic speech recognition (ASR) systems for use by under-served speaker populations. A task domain involving Indian farmers accessing information on agricultural commodities through a spoken dialog system in multiple languages is presented. To facilitate the development of ASR system for this domain, a speech corpus was collected in rural areas from speakers of four languages over wireless cellular channels. This paper investigates the problem of ASR acoustic modelling for this task domain. Continuous density hidden Markov model (CDHMM) and subspace Gaussian mixture model (SGMM) [1] based techniques are used to train acoustic models in four languages: Assamese, Bengali, Hindi and Marathi. Issues relating to limited linguistic resources with their impact on ASR word accuracy for these languages are addressed.
['Aanchan Mohan', 'S. Umesh', 'Richard C. Rose']
Subspace based for Indian languages