• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.86-93
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• 1997

With recent development of speech recognition technology and multimedia computer systems, more potential applications of voice will become a reality. In this paper, we implement speech interface on the windows95 environment for practical use fo multimedia computers with voice. Speech interface is made up of three modules, that is, speech input and detection module, speech recognition module, and application module. The speech input and etection module handles th elow-level audio service of win32 API to input speech data on real time. The recognition module processes the incoming speech data, and then recognizes the spoken command. DTW pattern matching method is used for speech recognition. The application module executes the voice command properly on PC. Each module of the speech interface is designed and examined on windows95 environments. Implemented speech interface and experimental results are explained and discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3267-3274
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• 2011

Recently, speech recognition technology has dramatically developed, with the increase in the user environment of various mobile devices and influence of a variety of speech recognition software. However, for speech recognition for multi-language, lack of understanding of multi-language lexical model and limited capacity of systems interfere with the improvement of the recognition rate. It is not easy to embody speech expressed with multi-language into a single acoustic model and systems using several acoustic models lower speech recognition rate. In this regard, it is necessary to research and develop a multi-language speech recognition system in order to embody speech comprised of various languages into a single acoustic model. This paper studied a system that can recognize Korean and English as International Phonetic Language (IPA), based on the research for using a multi-language acoustic model in mobile devices. Focusing on finding an IPA model which satisfies both Korean and English phonemes, we get 94.8% of the voice recognition rate in Korean and 95.36% in English.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.250-253
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• 1998

In digital communicatioin networks, speech recognition systems conventionally reconstruct speech followed by extracting feature [parameters. In this paper, we consider a useful approach by incorporating speech coding parameters into the speech recognizer. Most speech coders employed in the networks represent line spectral pairs as spectral parameters. In order to improve the recognition performance of the LSP-based speech recognizer, we introduce two different ways: one is to devise weighed distance measures of LSPs and the other is to transform LSPs into a new feature set, named a pseudo-cepstrum. Experiments on speaker-independent connected-digit recognition showed that the weighted distance measures significantly improved the recognition accuracy than the unweighted one of LSPs. Especially we could obtain more improved performance by using PCEP. Compared to the conventional methods employing mel-frequency cepstral coefficients, the proposed methods achieved higher performance in recognition accuracies.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.287-289
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• 2005

This paper presents a hybrid neural network system which used a Self-Organizing Map and Multilayer Perceptron for the problem of Malay syllables speech recognition. The novel idea in this system is the usage of a two-dimension Self-organizing feature map as a sequential mapping function which transform the phonetic similarities or acoustic vector sequences of the speech frame into trajectories in a square matrix where elements take on binary values. This property simplifies the classification task. An MLP is then used to classify the trajectories that each syllable in the vocabulary corresponds to. The system performance was evaluated for recognition of 15 Malay common syllables. The overall performance of the recognizer showed to be 91.8%.

• Speech Sciences
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• v.1
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• pp.277-283
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• 1997

In this paper, we present a robust recognition algorithm based on the Wiener filtering method as a research tool to develop the Korean Speech recognition system. We especially used Wiener filtering method in cepstrum-domain, because the method in frequency-domain is computationally expensive and complex. Evaluation of the effectiveness of this method has been conducted in speaker-independent isolated Korean digit recognition tasks using discrete HMM speech recognition systems. In these tasks, we used 12th order weighted cepstral as a feature vector and added computer simulated white gaussian noise of different levels to clean speech signals for recognition experiments under noisy conditions. Experimental results show that the presented algorithm can provide an improvement in recognition of as much as from $5\%\;to\;\20\%$ in comparison to spectral subtraction method.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1E
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• pp.3-11
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• 2002

Broadcast news transcription is one of the hardest tasks in speech recognition because broadcast speech signals have much variability in speech quality, channel and background conditions. We developed a Korean broadcast news speech recognizer. We used a morpheme-based dictionary and a language model to reduce the out-of·vocabulary (OOV) rate. We concatenated the original morpheme pairs of short length or high frequency in order to reduce insertion and deletion errors due to short morphemes. We used a lexicon with multiple pronunciations to reflect inter-morpheme pronunciation variations without severe modification of the search tree. By using the merged morpheme as recognition units, we achieved the OOV rate of 1.7% comparable to European languages with 64k vocabulary. We implemented a hidden Markov model-based recognizer with vocal tract length normalization and online speaker adaptation by maximum likelihood linear regression. Experimental results showed that the recognizer yielded 21.8% morpheme error rate for anchor speech and 31.6% for mostly noisy reporter speech.

• MALSORI
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• no.61
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• pp.113-123
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• 2007

The environmental differences between training and testing mode are generally considered to be the critical factor for the performance degradation in speaker recognition systems. Especially, general speaker recognition systems try to get as clean speech as possible to train the speaker model, but it's not true in real testing phase due to environmental and channel noise. So in this paper, the new method of weighting the frame-based likelihood according to frame SNR is proposed in order to cope with that problem. That is to make use of the deep correlation between speech SNR and speaker discrimination rate. To verify the usefulness of this proposed method, it is applied to the context dependent speaker identification system. And the experimental results with the cellular phone speech DB which is designed by ETRI for Koran speaker recognition show that the proposed method is effective and increase the identification accuracy by 11% at maximum.

• ETRI Journal
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• v.32 no.1
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• pp.160-162
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• 2010

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

• Industrial Engineering and Management Systems
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• v.13 no.3
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• pp.267-277
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• 2014

In this paper, we propose an educational system that involves a business game simulator and related curriculum. To develop these two elements, we examined the decision-making process related to business management and identified some significant skills thereby. In addition, we created an original simulator, named BizLator (http://bizlator.com), to help students develop these skills efficiently. Next, we developed a curriculum suitable for the simulator. We confirmed the effectiveness of the simulator and curriculum in a business-game-based class at Aoyama Gakuin University in Tokyo. On the basis of this, we compared our education system with a conventional system. This allowed us to identify advantages of and issues with our proposed system. Furthermore, we proposed a speech recognition support system named BizVoice in order to provide the teachers with more meaningful feedback, such as level of students' understanding. Concretely, BizVocie fetches students' speech of discussion during the game and converts the voice data to text data with speech recognition technology. Finally, teachers can grasp students' parameters of understanding, and thereby, the students also can take more effective class using BizLator. We also confirmed the effectiveness of the system in the class of Aoyama Gakuin Universiry.