• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.7 no.1
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• pp.43-49
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• 1996

In this study, remote articulation training system which connects the hearing disabled trainee and the speech therapist via B-ISDN is introduced. The hearing disabled does not have the hearing feedback of his own pronuciation, and the chance of watching his speech organs movement trajectory will offer him the self-training of articulation. So the system has two purposes of self articulation training and trainer's on-line checking in remote place. We estimate the vocal tract articultory movements from the speech signal using inverse modelling and display the movement trajectoy on the sideview of human face graphically. The trajectories of trainees articulation is displayed along with the reference trajectories, so the trainee can control his articulating to make the two trajectories overlapped. For on-line communication and ckecking training record the system has the function of video conferencing and tranferring articulatory data.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.528-533
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• 2010

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variation on the speech signal and the speech recognition system were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, a training method that cover the speech variations is proposed to develop the emotionally robust speech recognition system. Experimental results from the isolated word recognition using HMM showed that propose method reduced the error rate of the conventional recognition system by 28.4% when emotional test data was used.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.

• Progress in Medical Physics
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• v.33 no.4
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• pp.136-141
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• 2022

Purpose: This study aimed to develop a breath control training system for breath-hold technique and respiratory-gated radiation therapy wherein the patients can learn breath-hold techniques in their convenient environment. Methods: The breath control training system comprises a sensor device and software. The sensor device uses a loadcell sensor and an adjustable strap around the chest to acquire respiratory signals. The device connects via Bluetooth to a computer where the software is installed. The software visualizes the respiratory signal in near real-time with a graph. The developed system can signal patients through visual (software), auditory (buzzer), and tactile (vibrator) stimulation when breath-holding starts. A motion phantom was used to test the basic functions of the developed breath control training system. The relative standard deviation of the maxima of the emulated free breathing data was calculated. Moreover, a relative standard deviation of a breath-holding region was calculated for the simulated breath-holding data. Results: The average force of the maxima was 487.71 N, and the relative standard deviation was 4.8%, while the average force of the breath hold region was 398.5 N, and the relative standard deviation was 1.8%. The data acquired through the sensor was consistent with the motion created by the motion phantom. Conclusions: We have developed a breath control training system comprising a sensor device and software that allow patients to learn breath-hold techniques in their convenient environment.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.2
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• pp.209-216
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• 1992

Deafs train articulation by observing mouth of a tutor, sensing tactually the motions of the vocal organs, or using speech training aids. Present speech training aids for deafs can measure only single speech parameter, or display only frequency spectra in histogram of pseudo-color. In this study, a speech training aids that can display subject's articulation in the form of a cross section of the vocal organs and other speech parameters together in a single system is to be developed and this system makes a subject know where to correct. For our objective, first, speech production mechanism is assumed to be AR model in order to estimate articulatory motions of the vocal organs from speech signal. Next, a vocal tract profile model using LP analysis is made up. And using this model, articulatory motions for Korean vowels are estimated and displayed in the vocal tract profile graphics.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.493-503
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• 2018

Recently, as living standards have improved, many people are becoming more interested in health, and self-training is increasing through exercise to prevent and manage pre-illness. In general, an imbalance of muscles causes asymmetry of posture, which can cause various diseases by accompanying an adjustment force, circulation action, displacement of internal organs, etc.. In this study, the development of fitness software that can be self - training among smart wears has attracted considerable attention in recent years. In this study, a technology was proposed for the commercialization of self - trainer fitness wear by a simulation through Android - based applications. Self - trainer fitness software was developed by combining a conductive polymer, fashion design, sewing, and electric and electronic technology to monitor the unbalance of the muscles during exercise and make smart wear that can calibrate the asymmetry by oneself. In particular, a polymer sensor was fabricated by deriving the optimal MWCNT concentration, and the electrode signal was collected by attaching the electrode to the optimal position, where the electrode signal line using the conductive fiber was designed and attached to collect the signal. A signal module that converts the bio-signals collected through electrical signal conversion and transmits them using Bluetooth communication was designed and manufactured. Self-trainer fitness software that can be commercialized was developed by combining noise cancellation with Android-based self-training application using a software algorithm method.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2275-2284
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• 2015

In this paper, the Walsh coded orthogonal training signals for 4 × 4 multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems are designed and the channel estimation equations are derived as a closed form, taking account of the inter training signal interference problems caused by the multi-path delayed signals. The performances of the proposed channel estimation method are analyzed and compared with the conventional methods[9,14] by using computer simulation. The simulation results show that the proposed methods has better performances, compared with the conventional methods[9,14]. As a result, the proposed method can be used for MIMO-OFDM systems with null sub-carriers.

• ETRI Journal
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• v.33 no.3
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• pp.423-433
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• 2011

This paper introduces an adaptive and integrated utterance verification (UV) framework using minimum verification error (MVE) training as a new set of solutions suitable for real applications. UV is traditionally considered an add-on procedure to automatic speech recognition (ASR) and thus treated separately from the ASR system model design. This traditional two-stage approach often fails to cope with a wide range of variations, such as a new speaker or a new environment which is not matched with the original speaker population or the original acoustic environment that the ASR system is trained on. In this paper, we propose an integrated solution to enhance the overall UV system performance in such real applications. The integration is accomplished by adapting and merging the target model for UV with the acoustic model for ASR based on the common MVE principle at each iteration in the recognition stage. The proposed iterative procedure for UV model adaptation also involves revision of the data segmentation and the decoded hypotheses. Under this new framework, remarkable enhancement in not only recognition performance, but also verification performance has been obtained.