An Application of Robust Syllable Segmentation to Syllable-centric Continuous Speech Recognition

Abstract

The goal of this article is to (a) build a robust (a) illustrate the  importance of verifications in both of the training process of a  prototype system using a knowledge-based syllable classifier;  and (b) highlight the importance of verifications in all of the  training of the network of a prototype system using an  understanding morphemes classification approach. A  powerful lead to a sudden is used to divide the speech stream  into syllables. A non-statistical approach based on attribute  delay (GD) de - noising and Phonetic Onset Point (VOP)  interpretation is used to achieve this. To syllabify the  chromatin structure that matches to the speech, the guidelines  can be used. As a consequence, a testing data archive is  created. The described train results is being used to train a  syllable-based speech recognition system. The test signal is  also segmented using the specified manner. Following that,  the segmentation data is merged through into syntactic search  region, which reduces both withstand higher or the probability  of word mistakes (WER). WERs of 3.6 per cent and 21.2  nearly half are found in the TIMIT as well as NTIMIT  databases, respectfully.