ISOLATED SPOKEN WORD RECOGNITION USING HIDDEN MARKOV MODEL

robotics and automation

The main aim of the project is to develop an isolated spoken word recognition system using Hidden Markov Model (HMM) and that will be implemented on a 3R robotic arm with a good accuracy at all the possible frequency range of human voice. Here different command words like left, right, ready, up, down etc. are recorded by the speaker which can be male or female and results are compared with different feature extraction methods. The spoken word recognition system is mainly divided into two major blocks. First part includes recording data base of the command signals and feature extraction of those recorded signals. Here we use Mel frequency cepstral coefficients and fundamental frequency as feature extraction methods. To obtain Mel frequency cepstral coefficients signal we have to go through the following: framing, applying window function, Fast Fourier transform, filter bank and then discrete cosine transform.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.