• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.9
• /
• pp.1210-1215
• /
• 2018

Recently, as the IoT(Internet of Things) and AI(Artificial Intelligence) technologies have developed, smart toys that can understand and act on the language of human beings are being studied. In this paper, we study voice learning using CNN(Convolutional Neural Network) by applying artificial intelligence based voice secretary technology to smart toy. When a human voice command gives, Smart Toy recognizes human voice, converts it into text, analyzes the morpheme, and conducts tagging and voice learning. As a result of test for the simulator program implemented using Python, no malfunction occurred in a single command. And satisfactory results were obtained within the selected simulation condition range.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
• /
• v.12 no.2
• /
• pp.152-157
• /
• 2001

Background : Electrolarynx, Esophageal voice, and Silicone voice prosthesis with tracheoesophageal(T-E) fistula have been used as vocal rehabilitating methods for the post-laryngectomized patients. Prosthetic rehabilitation of voice after total laryngectomy has gained wide acceptance and has become a common practice in many clinics since the pioneering works of Singer and Blom In 1979. Since the introduction of tracheo-esophageal puncture and application of Blom Singer$\circledR$ voice prosthesis in 1980, several reliable voice prostheses have been developed and are successfully being used. Objectives : Even though quality of voice produced by Silicone voice prosthesis with T-E fistula is superior to other modalities, it still has some disadvantages. We devised a new cannulatyped silicone voice prosthesis. Methods : 1) Devising a new prototype of cannula-typed silicone voice prosthesis. 2) Application of the prototype using canine animal model(laryngectormized dog) and fitting trial on human patient whose previously inserted Silicone voice prosthesis is not functioning due to presumed fungal infection. Discussion : Final form of prototype was made after several times of major and minor modifications. Insertion of the newly developed Cannula-typed Silicone voice prosthesis on canine animal model and human trial were done without any difficulty. There were no serious leakage of saliva or food during swallowing. Conclusion : The newly developed Cannula-typed Silicone voice prosthesis(So-Mang$\circledR$) and the modified replacement method will further improve the results of post-laryngectomized prosthetic voice rehabilitation. Long-term animal study and human trial are planned in the near future.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.2
• /
• pp.137-146
• /
• 2019

This study proposed a new technique to control of mobile robot based on voice command for (Human-Robot Cooperative operation in manufacturing precess). High performance voice recognition and control system was designed In this paper for smart factory. robust voice recognition is essential for a robot to communicate with people. One of the main problems with voice recognition robots is that robots inevitably effects real environment including with noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we describe an robust voice recognition system which can robustly recognize voice by adults and students in noisy environments. It is illustrated by experiments the voice recognition performance of mobile robot placed in a real noisy environment.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.4
• /
• pp.215-222
• /
• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.2
• /
• pp.196-202
• /
• 2003

The object of this article is to study the plausibility of applying human voice warning system to automobiles. Human voice is considered the best tool for warning system in automobiles. For the purpose of comprehending the specific characteristics of relation between noises and properties of the automobiles indoors and voice warning system researcher performed FRF test in order to examine the characteristics of voice output, and FEM simulation to learn the specific properties of the car indoors. And furthermore, surveyed the quality of voice output, using the written inquiry to examine members. The result of the study shows that it is much possible to apply voice warning system to automobiles.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.9
• /
• pp.825-831
• /
• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.61-64
• /
• 2003

In this paper, we implement PAMD(Positive Answering Machine Detection) for discrimination human and ARS. We are used Grunt detection, Glitch Noise detection and Tone detection for PAMD. It distinguishes voice signals from ring-back tone and glitch noise respectively. And as a second step, it judges whether human responses or ARS responses after integrating pattern changes like initial response period, the number of voice data, each time of voice data period and glitch noise. The accuracy is about 9375 in ASR and about 98％ in Mobile phone.

• Journal of the Speleological Society of Korea
• /
• no.88
• /
• pp.1-7
• /
• 2008

Recently, it is one of very interest technology of Human Computer Interaction(HCI). Nowadays, it is easy to find out that, for example, inside SF movies people has talking to computer. However, there are difference between CPU language and ours. So, we focus on connecting to CPU. For 30 years many scientists experienced in that technology. But it is really difficult. Our project goal is making that CPU could understand human voice. First of all the signal through a voice sensor will move to BCD (binary code). That elevator helps out people who wants to move up and down. This product's point is related with people's safety. Using a PWM for motor control by ATmega16, we choose a DC motor to drive it because of making a regular speed elevator. Furthermore, using a voice identification module the elevator driven by voice sensor could operate well up and down perfectly from 1st to 10th floor by PWM control with ATmega16. And, it will be clearly useful for high-rise vertical shift with voice recognition sensor driven.

• Journal of Advanced Navigation Technology
• /
• v.14 no.3
• /
• pp.375-383
• /
• 2010

In this paper, the correlation between voice vowels and human body is analysed for the voice therapy and diagnosis. Using vowels('a', 'e', 'i', 'o', 'u'), the vibration signals in head, chest and belly is measured with the voice signal. As the result, it is shown that body characteristics can be checked from some vowels, and the correlation coefficient of body vibration signal and BMI(body mass index) is computed. From the result, using voice signal and body vibrations, the body diagnosis model is proposed.

• Science of Emotion and Sensibility
• /
• v.23 no.2
• /
• pp.61-74
• /
• 2020

Voice user interface has become a commercially viable and extensive interaction mechanism with the development of voice assistants. Despite the popularity of voice assistants, the academic community does not utterly understand about what, when, and how users chat with them. Chatting with a voice assistant is crucial as it defines how a user will seek the help of the assistant in the future. This study aims to cover the essence and construct of conversational AI, to develop a classification method to deal with user utterances, and, most importantly, to understand about what, when, and how Chinese users chat with voice assistants. We collected user utterances from the real conventional database of a commercial voice assistant, NetEase Sing in China. We also identified different utterance categories on the basis of previous studies and real usage conditions and annotated the utterances with 17 labels. Furthermore, we found that the three top reasons for the usage of voice assistants in China are the following: (1) greeting, (2) function, and (3) music. Chinese users like to interact with voice assistants at night from 7 PM to 10 PM, and they are polite toward the assistants. The whole percentage of negative feedback utterances is less than 6%, which is considerably low. These findings appear to be useful in voice interaction designs for intelligent hardware.