REAL-TIME CONTROL OF MOBILE ROBOT USING HMM-BASED SPEECH RECOGNITION SYSTEM

Year 2017, Volume: 18 Issue: 5, 897 - 907, 31.12.2017

Hayrettin Toylan Erol Türkeş Evren Çağlarer

https://doi.org/10.18038/aubtda.346121

Abstract

Human-robot
interaction (HRI) is a significant area of interest in robotics which has
attracted a wide variety of studies in recent years. In order to provide
natural human-robot interaction, robots will have to acquire the skills to
detect and to integrate meaningfully information from multiple modalities. In
this paper, a practical speech-controlled mobile robot car system is presented
and discussed. In this study the developed Hidden Markov Model (HMM) with
separate word recognition system and real-time control were obtained on a
mobile robot. Mel-Frequency Cepstral Coefficients (MFCC) were applied as
features for the control design of mobile robot. In the study, 270 speech commands
(İLERİ=forward, GERİ=backward, DUR=stop, SAĞA=right, SOLA=left) which are
collected from 54 different people were applied to a series of mathematical
operations and 12 cepstral coefficients were derived. Therefore, a database was
generated by 12 cepstral coefficients. Thus, HMM model was trained and tested
according to database. Speech data were classified in two groups as 90%
training data and 10% test data. The recognition success rate of test commands
was measured 94%.　

Keywords

Hidden markov model, MFCC, Speech recognition, Mobile robot, Robot

References

• Pfeifer R, Bongard J. How the body shapes the way we think: A new view of intelligence. London, England: MIT Press, 2006.