Dynamic Pronunciation Modelling for Unsupervised Learning of ASR Systems

Abstract

ABSTRACTThere is a large gap between the capabilities of the human beings and the automatic speech recognition (ASR) systems in recognizing pronunciation variations. ASR systems learn from labelled speech corpus, whereas the humans use “Everyday Speech” for adapting pronunciation variability. Labelling huge speech corpus in real time is impracticable, expensive, and time-consuming. In this paper, we present an algorithm using unsupervised learning techniques for adapting the easily available “Everyday Speech”. The algorithm is implemented using Java. The data sets are extracted from CMUDICT pronunciation directory, TIMIT database, and “The Hindu” daily newspaper. The results have shown a significant improvement in word error rate (WER) measurements over the existing ASR system. The addition of dynamic pronunciation model enables the ASR system to learn from the unlabelled “Everyday Speech” and makes it inexpensive and fast.