Abstract:
This paper presents a spontaneous speech
recognition system for Myanmar language. Automatic
speech recognition (ASR) on some controlled speech
has achieved almost human performance. However, the
performance of spontaneous speech is drastically
decreased due to the diversity of speaking styles, speak
rate, presence of additive and non-linear distortion,
accents and weakened articulation. In this study, we
built a recognizer for Myanmar Interview speech by
using the classical Gaussian Mixture Model based
Hidden Markov Model (HMM-GMM) approach. We
invested that the effect of variation on acoustic feature
and number of senones and Gaussian densities on
Myanmar Interview speech. According to these
experiments, we achieved the best Word Error Rate
(WER) of 20.47%.
