Subspace Gaussian Mixture Model for Continuous Urdu Speech Recognition using Kaldi

Abstract

Automatic Speech Recognition Systems (ASR) have significantly improved in recent years, where deep learning is playing an important role in the development of end to end ASR's. ASR is the task of converting spoken language into computer readable text. ASRs are becoming ever more prevalent way to interact with technology, thereby significantly closing the gap in terms of how humans interact with computers, making it more natural. Urdu is an under resourced language, for which training such a system requires a huge amount of data that is not readily available. In this paper we present improvements to the architecture of a statistical automatic speech recognition system for which the components..