• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.49-56
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• 2004

A low power consuming voice signal processing and control module was designed using a small $\mu$-controller for use in a totally implantable middle ear system. The module was designed that it can control the implanted system as well as process the fitting algorithm of input sound signal. In ordinary operation mode, the $\mu$-controller processes the applied sound signal for compensating the hearing loss of the patients. When the control signal is applied from the IR receiving module, the $\mu$-controller interrupts the signal processing and executes the order of the control signals such as power on/off, volume up/down. The designed module was implemented and verified the performance of the system through several experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.708-717
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• 2017

Tracking of sound-location is widely used in various area such as intelligent CCTV, video conference and voice commander. In this paper we introduce the real-time sound-location tracking method for smart lecture system using TDoA(Time Difference of Arrival) with orthogonal microphone array on the ceiling. Through discussion on some models of TDoA detection, cross correlation method using linear microphone array is proposed. Orthogonal array with 5 microphone could detect omni direction of sound-location. For real-time detection we adopt the threshold of received energy for eliminating no-voice interval, signed cross correlation for reducing computational complexity. The detected azimuth angles are processed using median filter for lowering the angle deviation. The proposed system is implemented with high performance MCU of TMS320F379D and MEMs microphone module and shows the accuracy of 0.5 and 6.5 in degree for white noise and lectured voice, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.281-281
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• 2000

This paper presents an implementation of speech recognition system for teaching an autonomous mobile robot. The use of human speech as the teaching method provides more convenient user-interface for the mobile robot. In this study, for easily teaching the mobile robot, a study on the autonomous mobile robot with the function of speech recognition is tried. In speech recognition system, a speech recognition algorithm using HMM(Hidden Markov Model) is presented to recognize Korean word. Filter-bank analysis model is used to extract of features as the spectral analysis method. A recognized word is converted to command for the control of robot navigation.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.553-560
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• 2006

Speech recognition has recently been investigated to produce more reliable recognition results in a noisy environment, by integrating diverse sources of information into the result derivation-level or producing new results through post-processing the prior recognition results. In this paper we propose a method which uses the user's usage patterns and the context information in speech command recognition for personal mobile devices to improve the recognition accuracy in a noisy environment. Sequential usage (or speech) patterns prior to the current command spoken are used to adjust the base recognition results. For the context information, we use the relevance between the current function of the device in use and the spoken command. Our experiment results show that the proposed method achieves about 50% of error correction rate over the base recognition system. It demonstrates the feasibility of the proposed method.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.509-516
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• 2017

People observe a lot of events in our environment and we do not have any difficulty to perceive events including speech perception. Like perception of biological motion, two main theorists have debated on speech perception. The purpose of this review article is to briefly describe speech perception and compare these two theories of speech perception. Motor theorists claim that speech perception is special to human because we both produce and perceive articulatory events that are processed by innate neuromotor commands. However, direct perception theorists claim that speech perception is not different from nonspeech perception because we only need to detect information directly like all other kinds of event. It is important to grasp the fundamental idea of how human perceive articulatory events for the convergence on speech engineering. Thus, this basic review of speech perception is expected to be able to used for AI, voice recognition technology, speech recognition system, etc.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1095-1098
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• 1999

We have developed a speaker dependent voice command system(VCS) to control the sunroof in the car using RSC-164 VRP(Voice Recognition Processor). VCS consists of control circuits, microphone, speaker and user switch box. The control circuits include RSC-164, input audio preamplifier, memory devices, and relay circuit for sunroof control. It is designed robustly in various car noisy situations like audio volume, air conditioner, and incoming noise when window or sunroof opened. Each two users can control the car sunroof using seven voice commands on the Super TVS model and five voice commands on the Onyx model. It works well when we drive the car at over 100 km/h with the sunroof opened.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1A
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• pp.9-17
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• 2004

Adapt ive and fixed step size PC (power control) schemes for accommodating voice, video, and data are evaluated according to the different PC command rates and their effects on integrated Voice/Video/Data are investigated. The required minimum power levels are derived as PC thresholds and the effects of PC errors on channel quality and radio 1 ink capacity are investigated. The services with high bit rates and low bit error rates can cause a significant effect on the radio link qualifies of the other types of traffic. The results show that the adapt ive step size PC scheme for voice/video/data services can achieve more capacity and cause less interference to the radio channels because less minimum PIL(Power Increment Level) is required for the specified radio link outage probability.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.726-734
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• 2007

Generally, a mobile robot is moved by original input programs. However, it is very hard for a non-expert to change the program generating the moving path of a mobile robot, because he doesn't know almost the teaching command and operating method for driving the robot. Therefore, the teaching method with speech command for a handicapped person without hands or a non-expert without an expert knowledge to generate the path is required gradually. In this study, for easily teaching the moving path of the autonomous mobile robot, the autonomous mobile robot with the function of speech recognition is developed. The use of human voice as the teaching method provides more convenient user-interface for mobile robot. To implement the teaching function, the designed robot system is composed of three separated control modules, which are speech preprocessing module, DC servo motor control module, and main control module. In this study, we design and implement a speaker dependent isolated word recognition system for creating moving path of an autonomous mobile robot in the unknown environment. The system uses word-level Hidden Markov Models(HMM) for designated command vocabularies to control a mobile robot, and it has postprocessing by neural network according to the condition based on confidence score. As the spectral analysis method, we use a filter-bank analysis model to extract of features of the voice. The proposed word recognition system is tested using 33 Korean words for control of the mobile robot navigation, and we also evaluate the performance of navigation of a mobile robot using only voice command.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.293-300
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• 2016

The Voice recognition is one of convenient methods to communicate between human and robots. This study proposes a speech recognition method using speech recognizers based on Hidden Markov Model (HMM) with a combination of techniques to enhance a biped robot control. In the past, Artificial Neural Networks (ANN) and Dynamic Time Wrapping (DTW) were used, however, currently they are less commonly applied to speech recognition systems. This Research confirms that the HMM, an accepted high-performance technique, can be successfully employed to model speech signals. High recognition accuracy can be obtained by using HMMs. Apart from speech modeling techniques, multiple feature extraction methods have been studied to find speech stresses caused by emotions and the environment to improve speech recognition rates. The procedure consisted of 2 parts: one is recognizing robot commands using multiple HMM recognizers, and the other is sending recognized commands to control a robot. In this paper, a practical voice recognition system which can recognize a lot of task commands is proposed. The proposed system consists of a general purpose microprocessor and a useful voice recognition processor which can recognize a limited number of voice patterns. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.99-113
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• 2020

In the future, robots and drones for the convenience of our lives in everyday life will increase. As a method for controlling this, a remote control or a human voice method is most commonly used. However, the remote control needs to be operated by a person and can not ignore ambient noise in the case of voice. In this paper, we propose an economical attitude information acquisition method to accurately acquire the posture information of the arm in real time under the assumption that the surround drones or robots can be controlled wirelessly with the posture information of the arm. For this purpose, the extended Kalman filter was used to eliminate the noise of the arm position information. in order to detect the arm movement, a low cost MEMS type sensor was applied to secure the economical efficiency of the apparatus. To increase the wear ability of the arm, We developed a compact and lightweight attitude information acquisition system by integrating all functions into one chip as much as possible. As a result, the real-time performance of 1 ms was secured and the extended Kalman filter was applied to acquire the accurate attitude information of the arm with noise removed and display the attitude information of the arm in real time. This provides a basis for generating commands using real-time attitude information of the arm.