Name
Abstract | ||
|---|---|---|
An approach for decision-level fusion for gesture and speech based human-robot interaction (HRI) is proposed. A rule-based method is compared with several machine learning approaches. Gestures and speech signals are initially classified using hidden Markov models, reaching accuracies of 89.6% and 84% respectively. The rule-based approach reached 91.6% while SVM, which was the best of all evaluated machine learning algorithms, reached an accuracy of 98.2% on the test data. A complete framework is deployed in real time humanoid robot (NAO) which proves the efficacy of the system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MMAR.2019.8864671 | 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR) |
Keywords | Field | DocType |
Gesture recognition,speech recognition,decision level fusion,HMM | Gesture,Computer science,Support vector machine,Gesture recognition,Speech recognition,Control engineering,Feature extraction,Test data,Hidden Markov model,Human–robot interaction,Humanoid robot | Conference |
ISBN | Citations | PageRank |
978-1-7281-0934-3 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neha Baranwal | 1 | 7 | 4.95 |
| Avinash Kumar Singh | 2 | 31 | 13.77 |
| Thomas Hellström | 3 | 64 | 10.98 |