Feature Extraction for Speech Classification
Shankari N1, Rajashree2
1Mrs. Shankari N, Assistant Professor, Department of ECE, NMAMIT, Nitte, Udupi district, Karnataka.
2Ms. Rajashree, Assistant Professor, Department of CSE, NMAMIT, Nitte, Udupi district, Karnataka.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4987-4989 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6830018520/2020©BEIESP | DOI: 10.35940/ijrte.E6830.018520

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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: Study of phonological Processes, Speech recognition, Speech Synthesis and language learning requires Automatic classification of class of sounds and automatic identification of sound classes. This paper focuses on identifying features efficient in discriminating different classes of sound such as analyzing spectral features such as distinctive frequency components by Linear Productive Coding technique and vocal tract length. Artificial Neural network and Random Forest Classification Technique is used to check effectiveness of identified feature with 10-fold cross validation. The proposed system is also aimed at improving performance of phoneme recognition system.
Keywords: Artificial Neural Network, Random Forest Classification, Cross Validation.
Scope of the Article: Sensor Networks, Actuators for Internet of Things.