Speech Recognition using Modular General Fuzzy Min-Max Neural Network

Abstract

In this paper, we propose the Modular General Fuzzy, Min-Max Neural Network (MMGFMM). MMGFMM contains number of modules of Modified General Fuzzy Min-Max Neural Network (MGFMM). In MGFMM, the transfer function of output layer of General Fuzzy Min-Max Neural Network (GFMM) is modified. The performance of GFMM, MGFMM and MMGFMM to recognition of spoken Marathi (Language spoken in the state of Maharashtra, India) digits is reported. MMGFMM has shown better average recognition accuracy compared to GFMM by 15.1% in speaker dependent mode and by 11.9% in speaker independent mode.

Indexing terms:

• Fuzzy systems
• Fuzzy min-max neural network
• Modular neural network
• K-nn decision rule
• Speech recognition

Additional information

Notes on contributors

Dharmpal Doye

D D Doye received his BE (Electronics) degree in 1988 and ME (Electronics) degree in 1993, from SGGS College of Engineering and Technology, Vishnupuri, Nanded (MS). Presently, he is working as Assistant Professor in Department of Electronics and Computer Science and Engineering, SGGS College of Engineering and Technology, Vishnupuri, Nanded. His field of research is Speech Recognition.

Trimbak Sontakke

T R Sontakke received his BE (Electrical Engineering) from Government College of Engineering, Aurangabad (MS), MTech from VRCE Nagpur (MS) and PhD from IIT, Bombay, Mumbai. He worked in TTT I, Bhopal for a period of ten years. He is currently working as Professor in Electronics with additional charge of Principal, SGGS College of Engineering and Technology, Vishnupuri, Nanded. He has been the recipient of two Gold Medals from Institution of Engineers, India for his research papers. His research fields are Speech Recognition, Neural Networks and Image Processing.