Isolated Telugu Speech Recognition On T-DSCC And DNN Techniques
Archek Praveen Kumar1, Neerudu Uma Maheshwari2, Y. Sangeetha3, P. Jyothi4
1Dr. Archek Praveen Kumar, Professor, HOD, Department of ECE, Malla Reddy College of Engineering for Women, Hyderabad, Telangana, India.
2Neerudu Uma Maheshwari, Associate Professor, Department of ECE, Malla Reddy College of Engineering for Women, Hyderabad, Telangana, India.
3Y. Sangeetha, Assistant Professor, Department of ECE, Malla Reddy College of Engineering for Women, Hyderabad, Telangana, India.
4P. Jyothi, Assistant Professor, Department of ECE, Malla Reddy College of Engineering for Women, Hyderabad, Telangana, India.
Manuscript received on 20 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 3419-3422 | Volume-8 Issue-11, September 2019. | Retrieval Number: K25440981119/2019©BEIESP | DOI: 10.35940/ijitee.K2544.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Communication is the major path to convey the information. Speech is the best mode for conveying the information. Human to human information can be exchanged through some particular language. But the interaction between human and machine is the major challenge which deals with ASR (Automatic speech recognition). This research recognizes speaker independent data which gives good results by using TDSCC (Teager energy operator delta spectral cepstral coefficients) feature extraction technique and DNN (Deep Neural Networks) feature classification technique. This paper also uses CASA technique for pre-processing the speech signals. This research is done by creating the database for 10 most speak able isolated words in Telugu.
Keywords: Isolated speech recognition, Telugu language, T-DSCC, DNN.
Scope of the Article: Speech interface; Speech processing