Abstract:
Myanmar language is a tonal and analytic
language. It can be considered as an under-resourced
language because of its linguistic resource availability.
Therefore, speech data collection is a very challenging
task in building Myanmar automatic speech
recognition. Today a lot of speech data are freely
available on the Internet and we can collect it easily.
Therefore, in this system, we take the advantages of
Internet and we use daily news from the Web in
building our speech corpus. In this paper, we will
present about the task of data collection, the effect of
Automatic Speech Recognition (ASR) performance
according to amount of training data, language model
size and error analysis of the experimental result. The
experiments will be developed using Hidden Markov
Model (HMM) with Gaussian Mixture Model (GMM)
and Subspace Gaussian Mixture Model (SGMM). As a
result, using our developed 5 hours training data, this
system achieves word error rate (WER) of 7.6% on
close test data and 31.9% on open test data with
HMM-SGMM.
