WAVELET TRANSFORMER FOR AUTOMATIC SPEECH RECOGNITION OF INDIAN LANGUAGES

Abstract

This research paper proposes a Wavelet
transformer for automatic speech recognition (WTASR) of
Indian languages. Automatic speech recognition systems are
developed for translating the speech signals into the
corresponding text representation. This translation is used in a
variety of applications like voice enabled commands, assistive
devices and bots, etc. There is a significant lack of efficient
technology for Indian languages. The speech signals suffer
from the problem of high and low frequency over different
times due to variation in speech of the speaker. Thus, wavelets
enable the network to analyze the signal in multiscale. The
wavelet decomposition of the signal is fed in the network for
generating the text. The transformer network comprises an
encoder decoder system for speech translation. The model is
trained on Indian language dataset for translation of speech
into corresponding text. The proposed method is compared with
other state of the art methods. The results show that the
proposed WTASR has a low word error rate and can be used
for effective speech recognition for Indian language.