RETRACTED ARTICLE: An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map

• Published in: Neural computing & applications Vol. 31; no. 5; pp. 1521 - 1531
• Main Authors: Lokesh, S., Malarvizhi Kumar, Priyan, Ramya Devi, M., Parthasarathy, P., Gokulnath, C.
• Format: Journal Article
• Language: English
• Published: London Springer London 03.05.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Probability and Statistics in Computer Science; S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing; Bidirectional Recurrent Neural Network (BRNN); Preprocessing; Multivariate Autoregressive (MAR); Self-Organizing Map (SOM); Classification; Automatic Tamil Speech Recognition; Feature extraction; Discrete Cosine Transformation (DCT); Savitzky–Golay Filter (SGF); Perceptual Linear Predictive (PLP)
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3466-5

Speech recognition is one of the entrancing fields in the zone of computer science. Exactness of speech recognition framework may decrease because of the nearness of noise exhibited by the speech signal. Consequently, noise removal is a fundamental advance in automatic speech recognition (ASR) system. ASR is researched for various languages in light of the fact that every language has its particular highlights. Particularly, the requirement for ASR framework in Tamil language has been expanded broadly over the most recent couple of years. In this work, bidirectional recurrent neural network (BRNN) with self-organizing map (SOM)-based classification scheme is suggested for Tamil speech recognition. At first, the input speech signal is pre-prepared by utilizing Savitzky–Golay filter keeping in mind the end goal to evacuate the background noise and to improve the signal. At that point, Multivariate Autoregressive based highlights by presenting discrete cosine transformation piece to give a proficient signal investigation. And in addition, perceptual linear predictive coefficients likewise separated to enhance the classification accuracy. The feature vector is shifted in measure, for picking the right length of feature vector SOM utilized. At long last, Tamil digits and words are ordered by utilizing BRNN classifier where the settled length feature vector from SOM is given as input, named as BRNN-SOM. The experimental analysis demonstrates that the suggested conspire accomplished preferable outcomes looked at over exist deep neural network–hidden Markov model algorithm regarding signal-to-noise ratio, classification accuracy, and mean square error.