• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.784-788
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• 2008

In this research, we propose a remote speech input unit, a new method of user-friendly speech input in speech recognition system. We focused the user friendliness on hands-free and microphone independence in speech recognition applications. Our module adopts two algorithms, the automatic speech detection and speech enhancement based on the microphone array-based beamforming method. In the performance evaluation of speech detection, within-200msec accuracy with respect to the manually detected positions is about 97percent under the noise environments of 25dB of the SNR. The microphone array-based speech enhancement using the delay-and-sum beamforming algorithm shows about 6dB of maximum SNR gain over a single microphone and more than 12% of error reduction rate in speech recognition.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.38-46
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• 1998

In this paper, we propose a remote speech input device, a new method of user-friendly speech input in spontaneous speech recognition system. We focus the user friendliness on hands-free and microphone independence in speech recognition applications. Our method adopts two algorithms, the automatic speech detection and the microphone array delay-and-sum beamforming (DSBF)-based speech enhancement. The automatic speech detection algorithm is composed of two stages; the detection of speech and nonspeech using the pitch information for the detected speech portion candidate. The DSBF algorithm adopts the time domain cross-correlation method as its time delay estimation. In the performance evaluation, the speech detection algorithm shows within-200 ms start point accuracy of 93%, 99% under 15dB, 20dB, and 25dB signal-to-noise ratio (SNR) environments, respectively and those for the end point are 72%, 89%, and 93% for the corresponding environments, respectively. The classification of speech and nonspeech for the start point detected region of input signal is performed by the pitch information-base method. The percentages of correct classification for speech and nonspeech input are 99% and 90%, respectively. The eight microphone array-based speech enhancement using the DSBF algorithm shows the maximum SNR gaing of 6dB over a single microphone and the error reductin of more than 15% in the spontaneous speech recognition domain.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.723-726
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• 2009

In this research, We simulated performances of error reduction algorithm for the speech signal based on the microphone array-based beamforming method in speech recognition system and analyzed its performance. Also, we processed speech signal adopted from microphone array and maximum signal to noise ratio for each channel, and then compared them with signal to noise ratio of speech signal. Speech recognition rate is improved from 54.2% to 61.4% in case 1 and is improved from 41.2% to 50.5% in case 2 of the lower signal to noise ratio. Therefore the average reduction rates are showed 15.7% in case 1.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.169-175
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• 1996

In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.133-136
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• 2002

VoiceXML is designed for creating audio dialogs that feature synthesized speech, degitized audio, recognition of spoken and DTMF key input, recording of spoken input, telephony, and mixed-initiative conversations. Uses the VoiceXML and there is a Place objective which does information home appliance machinery and tools control. When it uses tile VoiceXML, il will be able to provide a bias characteristic to the user The XML base the gearing with different civil official system is possible. With studying YoiceXML and OSGi, this paper has designed and implemented the control architecture of Information home appliances.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Journal of the HCI Society of Korea
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• v.11 no.2
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• pp.15-22
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• 2016

The ways to interact with digital appliances are becoming more diverse. Users can control appliances using a remote control and a touch-screen, and appliances can send users feedback through various ways such as sound, voice, and visual signals. However, there is little research on how to define which output method to use for providing feedback according to the user' input method. In this study, we designed an experimental study that seeks to identify how to appropriately match the output method - voice and sound - based on the user input - voice and button. We made four types of interaction with two kinds input methods and two kinds of output methods. For the four interaction types, we compared the usability, perceived satisfaction, preference and suitability. Results reveals that the output method affects the ease of use and perceived satisfaction of the input method. The voice input method with sound feedback was evaluated more satisfying than with the voice feedback. However, the keying input method with voice feedback was evaluated more satisfying than with sound feedback. The keying input method was more dependent on the output method than the voice input method. We also found that the feedback method of appliances determines the perceived appropriateness of the interaction.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.14-18
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• 2006

The role of dialogue manager is to select proper actions based on observed environment and inferred user intention. This paper presents stochastic model for dialogue manager based on Markov decision process. To build a mixed initiative dialogue manager, we used accumulated user utterance, previous act of dialogue manager, and domain dependent knowledge as the input to the MDP. We also used dialogue corpus to train the automatically optimized policy of MDP with reinforcement learning algorithm. The states which have unique and intuitive actions were removed from the design of MDP by using the domain knowledge. The design of dialogue manager included the usage of natural language understanding and response generator to build short message based remote control of home networked appliances.