Empirical approach for a novel PCC-MFCC and TS-CTRNN based speech recognition system

Currently, in the field of speech signal processing, a large amount of research has been conducted. Especially, there is a growing interest in the automatic speech recognition (ASR) technology field. Nevertheless, owing to a noisy environment, traditional systems have low performance. Hence, a novel Tanh sigmoid-centred continuous time recurrent neural network (TS-CTRNN)-cantered speech recognition system (SRS) is proposed in this work. Two phases are incorporated by the proposed technique. Firstly, in the input audio, the frequency spectrum is scrutinised. Next, the spectrum is pre-processed. Afterwards, from the pre-processed signal, the features are extracted. The next phase begins with pre-processing and word embedding where the label is taken as the input. At last, the output obtained from both phases is inputted into the TS-CTRNN, which predicts speech in the format of text. The experimental outcomes exhibit that when analogised to the created ASR system, the enhanced virtue of noise elimination methodology and TS-CTRNN-cantered recognition provides a better relative enhancement of accuracy to (96.89%).