A robust statistical-based speaker's location detection algorithm in a vehicular environment
EURASIP Journal on Applied Signal Processing
The state of play in machine/environment interactions
Artificial Intelligence Review
Ontology-based speech act identification in a bilingual dialog system using partial pattern trees
Journal of the American Society for Information Science and Technology
Behavior detection using confidence intervals of hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human robot interactions: towards the implementation of adaptive strategies for robust communication
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Botnet traffic detection using hidden Markov models
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
P2P hierarchical botnet traffic detection using hidden Markov models
Proceedings of the 2012 Workshop on Learning from Authoritative Security Experiment Results
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We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.