Open AccessDevelopment of End – to – End Encoder - Decoder Model Applying Voice Recognition System in Different Channels
Author(s) -
L. MaryGladence,
V. Rajalakshmi
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1267.0982s1119
Subject(s) - computer science , speech recognition , word error rate , decoding methods , encoder , metric (unit) , preprocessor , word (group theory) , end to end principle , artificial intelligence , artificial neural network , sequence (biology) , algorithm , pattern recognition (psychology) , mathematics , engineering , operations management , geometry , biology , genetics , operating system
the dissimilarity in recognizing the word sequence and their ground truth in different channels can be absorbed by implementing Automatic Speech Recognition which is the standard evaluation metric and is encountered with the phenomena of Word Error Rate for various measures. In the model of 1ch, the track is trained without any preprocessing and study on multichannel end-to-end Automatic Speech Recognition envisaged that the function can be integrated into (Deep Neural network) – based system and lead to multiple experimental results. More so, when the Word Error Rate (WER) is not directly differentiable, it is pertinent to adopt Encoder – Decoder gradient objective function which has been clear in CHiME-4 system. In this study, we examine that the sequence level evaluation metric is a fair choice for optimizing Encoder – Decoder model for which many training algorithms is designed to reduce sequence level error. The study incorporates the scoring of multiple hypotheses in decoding stage for improving the decoding result to optimum. By this, the mismatch between the objectives is resulted in a feasible form to the maxim. Hence, the study finds the result of voice recognition which is most effective for adaptation.