Article: Empirical approach for a novel PCC-MFCC and TS-CTRNN based speech recognition system Journal: International Journal of Advanced Mechatronic Systems (IJAMECHS) 2025 Vol.12 No.3 pp.186 - 196 Abstract: 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%). Inderscience Publishers - linking academia, business and industry through research

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

Authors: Shivani Trivedi; Sanjay Patidar; Rohit Rastogi

Addresses: Department of CSE, ABES Engineering College, Ghaziabad, U.P., India ' Department of Software Engineering, Delhi Technological University, Shahbad Daulatpur, Bawana Road, Delhi-110042, India ' Department of CSE, ABES Engineering College, Ghaziabad, U.P., India

Abstract: 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%).

Keywords: Pearson correlation coefficient based Mel frequency cepstral coefficient; PCC-MFCC; entropy-based Wiener filter; Tanh sigmoid based continuous time recurrent neural network; TS-CTRNN; XLM-RoBERTa; automatic speech recognition; ASR.

DOI: 10.1504/IJAMECHS.2025.147094

International Journal of Advanced Mechatronic Systems, 2025 Vol.12 No.3, pp.186 - 196

Received: 27 Aug 2024
Accepted: 05 Mar 2025
Published online: 10 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

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

Keep up-to-date