Real Time Speech Recognition System For Robotic Control Applications Using An Ear Microphone
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Real-time Speech Recognition System for Robotic Control Applications Using an Ear-microphone
This study is part of an ongoing research started in 2004 at the Naval Postgraduate School (NPS) investigating the development of a human-machine interface commandand- control package for controlling robotic units in operational environments. An ear microphone is used to collect the voice-activated commands providing hands-free control instructions in noisy environments [Kurcan, 2006; Bulbuller, 2006]. This study presents the hardware implementation of a theoretical Isolated Word Recognition (IWR) system designed in an earlier study. The recognizer uses a short-term energy and zero-crossing based detection scheme, and a discrete Hidden Markov model recognizer designed to recognize seven isolated words. Mel frequency cepstrum coefficients (MFCC) are used for discriminating features in the recognizer phase. The hardware system implemented uses commercial off-the-shelf (COTS) electronic components, in-ear microphone, is portable and costs under $50.00. The implemented speech capturing system uses the ear-microphone and the Si3000 Audio Codec to capture and sample speech clearly. The microprocessor processes the detected speech in real-time. The microprocessor's I/O devices work effectively with the audio codec and computer for sampling and training, without communication problems or data loss. The current implementation uses 1.181 msec to process each 15 msec data frame. Resulting recognition performances average around 73.72%.
Robot Intelligence Technology and Applications 2012
Author: Jong-Hwan Kim
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-04-03
In recent years, robots have been built based on cognitive architecture which has been developed to model human cognitive ability. The cognitive architecture can be a basis for intelligence technology to generate robot intelligence. In this edited book the robot intelligence is classified into six categories: cognitive intelligence, social intelligence, behavioral intelligence, ambient intelligence, collective intelligence and genetic intelligence. This classification categorizes the intelligence of robots based on the different aspects of awareness and the ability to act deliberately as a result of such awareness. This book aims at serving researchers and practitioners with a timely dissemination of the recent progress on robot intelligence technology and its applications, based on a collection of papers presented at the 1st International Conference on Robot Intelligence Technology and Applications (RiTA), held in Gwangju, Korea, December 16-18, 2012. For a better readability, this edition has the total 101 papers grouped into 3 chapters: Chapter I: Cognitive Intelligence, Social Intelligence and Behavioral Intelligence, Chapter II: Ambient Intelligence, Collective Intelligence and Genetic Intelligence, Chapter III: Intelligent Robot Technologies and Applications.
Computational Methods in Neural Modeling
Author: José Mira
language: en
Publisher: Springer Science & Business Media
Release Date: 2003-05-22
The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003. The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.