• Journal of the Korean Society for Railway
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• v.14 no.2
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• pp.130-136
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• 2011

This paper suggests realization method for real-time speech recognition using VoiceXML in telephony environment based on SIP for Railroad Reservation Service. In this method, voice signal incoming through PSTN or Internet is treated as dialog using VoiceXML and the transferred voice signal is processed by Speech Recognition System, and the output is returned to dialog of VoiceXML which is transferred to users. VASR system is constituted of dialog server which processes dialog, APP server for processing voice signal, and Speech Recognition System to process speech recognition. This realizes transfer method to Speech Recognition System in which voice signal is recorded using Record Tag function of VoiceXML to process voice signal in telephony environment and it is played in real time.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.179-184
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• 2013

A voice recognition technology as a technology fundament plays an important role in medical devices with smart functions. This paper describes the implementation of a control system that can be utilized as a part of illumination equipment for medical applications (IEMA) based on a voice recognition. The control system can essentially be divided into five parts, the microphone, training part, recognition part, memory part, and control part. The system was implemented using the RSC-4x evaluation board which is included the micro-controller for voice recognition. To investigate the usefulness of the implemented control system, the experiments of the recognition rate was carried out according to the input distance for voice recognition. As a result, the recognition rate of the control system was more than 95% within a distance between 0.5 and 2m. The result verified that the implemented control system performs well as the smart control system based for an IEMA.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.51-57
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• 2001

How to promote students interest is very important in undergraduate engineering education. One of the techniques for achieving this is select appropriate projects and to integrated them with regular courses. In this paper, a voice recognition system for autonomous robots is proposed as a project to educate students about microprocessors efficiently. The proposed system consists of a microprocessor and a voice recognition processor that can recognize a limited unmber of voice patterns. The commands of autono-mous robots are classified and are organized such that one voice recognition processor can distinguish robot commands under each directory. Thus. the proposed system can distinguish more voice commands than one voice recognition processor can. A voice com-mand systems for three autonomous robots is implemented with a microprocessor Inter 80CI196KC and a voice recognition processor HM2007. The advantages in integrating this system with regular courses are also described.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.250-258
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• 2015

In this research, we proposed a new approach to implement the real-time motion control of biped robot based on voice command for unmanned FA. Voice is one of convenient methods to communicate between human and robots. To command a lot of robot task by voice, voice of the same number have to be able to be recognition voice is, the higher the time of recognition is. In this paper, a practical voice recognition system which can recognition 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. Given biped robots, each robot task is, classified and organized such that the number of robot tasks under each directory is net more than the maximum recognition number of the voice recognition processor so that robot tasks under each directory can be distinguished by the voice recognition command. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.58-63
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• 1991

This paper describes development of small scale voice dialing system using TMS320C30. Recognition vocabuliary is used 50 department name within university. In vocabulary below the middle scale, word unit recognition is more practice than phoneme unit or syllable unit recognition. In this paper, we performend recognition and model generation using DMS(Dynamic Multi-Section) and implemeted voice dialing system using TMS320C30. As a result of recognition, we achieved a 98% recognition rate in condition of section 22 and weight 0.6 and recognition time took 4 seconds.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.89-97
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• 2021

Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.207-213
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• 2011

In this paper, we present a study on the real-time implementation of mobile robot to which the interactive voice recognition technique is applied. The speech command utters the sentential connected word and asserted through the wireless remote control system. We implement an automatic distance speech command recognition system for voice-enabled services interactively. We construct a baseline automatic speech command recognition system, where acoustic models are trained from speech utterances spoken by a microphone. In order to improve the performance of the baseline automatic speech recognition system, the acoustic models are adapted to adjust the spectral characteristics of speech according to different microphones and the environmental mismatches between cross talking and distance speech. We illustrate the performance of the developed speech recognition system by experiments. As a result, it is illustrated that the average rates of proposed speech recognition system shows about 95% above.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.949-954
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• 2003

In this study, Automatic Speech Recognition Application System is designed and implemented to realize transformation from present GUI-centered web services to VUI-centered web service. Due to ASP's restriction with web in reusability and portability, in this study, Automatic Speech Recognition Application System with Javabeans Component Architecture is devised and studied. Also the voice web browsing which is able to transfer voice and graphic information simultaneously is studied using Remote AWT(Abstract Windows Toolkit).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.991-998
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• 2010

In realization of wireless home network system using speech recognition in indoor voice recognition environment, background noise and reverberation are two main causes of digression in voice recognition system. In this study, the home network system resistant to reverberation and background noise using voice section detection method based on spectral entropy in indoor recognition environment is to be realized. Spectral subtraction can reduce the effect of reverberation and remove noise independent from voice signal by eliminating signal distorted by reverberation in spectrum. For effective spectral subtraction, the correct separation of voice section and silent section should be accompanied and for this, improvement of performance needs to be done, applying to voice section detection method based on entropy. In this study, experimental and indoor environment testing is carried out to figure out command recognition rate in indoor recognition environment. The test result shows that command recognition rate improved in static environment and reverberant room condition, using voice section detection method based on spectral entropy.