REAL-TIME CONTROL OF MOBILE ROBOT USING HMM-BASED SPEECH RECOGNITION SYSTEM
| Autor: | Erol Türkeş, Hayrettin Toylan, Evren Çağlarer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Hidden markov model
MFCC Speech recognition Mobile robot Robot Computer science Robot Speech recognition Mühendislik Engineering Real-time Control System Mobile robot lcsh:Technology (General) Hidden Markov model business.industry Robotics General Medicine ComputingMethodologies_PATTERNRECOGNITION MFCC lcsh:TA1-2040 Hidden markov model Word recognition lcsh:T1-995 Mel-frequency cepstrum Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) Test data |
| Zdroj: | Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering, Vol 18, Iss 5, Pp 897-907 (2017) Volume: 18, Issue: 5 897-907 Anadolu University Journal of Science and Technology A-Applied Sciences and Engineering |
| ISSN: | 2146-0205 1302-3160 |
| Popis: | 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 (ILERI=forward, GERI=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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|