• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.167-170
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• 2002

본 논문에서는 전화망 환경에서의 한국어 숫자음 인식을 위한 특징 파라미터 추출, 음향 모델링 방식을 결정하기 위하여 HTK 툴을 사용한 4 연숫자음 인식실험 결과를 기술한다. 또한, 실험 결과를 토대로 빈번하게 발생하는 숫자음에 대해서 오류율을 분석하였다. 숫자 모델로는 left context biword 모델과 triword 모델을 사용하였으며, 상태수와 mixture 수를 바꾸어 인식 실험을 수행한 결과, triword 모델이 biword 모델보다 인식율이 높은 것으로 나타났으며, substitution 에러율은 " 이<->" 에서 가장 높은 에러가 발생하는 결과를 얻을 수 있다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.111-114
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• 2002

음성인식에서 발화검증은 비인식대상어휘(OOV)를 기각시키고, 인식대상어휘라도 오인식 가능성이 높은 결과를 기각시키는 기술을 말한다. 본 논문에서는 혼동가능성 높은 숫자쌍들이 존재하는 한국어 연결 숫자 인식에서 발화검증 결과로 숫자열 기각시 오인식 가능성이 높은 숫자열을 그냥 기각시키는 대신에 대체오류를 수정하여 인식성능을 향상시키고자 하였다. N-best decoding 결과에 따르면 $2^{nd}\;best$ 나 $3^{rd}\;best$안에 대부분의 제대로 된 인식결과들이 포함된다. 따라서, N-best decoding을 이용해, 숫자열 기각시 $2^{nd}\;best$ 숫자열로 대체된 것이라고 가정한 후, 개별숫자 log likelihood ratio(LLR)과 N-best 기반의 숫자열 LLR[3] 등을 함께 고려한 신뢰도 측정방식에 의해 그 가정이 맞다고 판단이 되면 $2^{nd}\;best$ 의 숫자열과 대체함으로써 부분적으로 오류를 수정하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.561-562
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• 2021

CNN (Convolutional Neural Network) is widely used to recognize various images. In this presentation, a single digit handwritten by humans was recognized by applying the CNN technique of deep learning. The deep learning network consists of a convolutional layer, a pooling layer, and a platen layer, and finally, we set an optimization method, learning rate and loss functions.

• Journal of Oriental Neuropsychiatry
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• v.25 no.3
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• pp.243-252
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• 2014

Objectives: This study was designed to investigate the dose-dependent effects of Gastrodia elata Blume for memory improvement. Methods: This study was a 12-weeks, double blind, and comparative clinical study. Those who were eligible worked with a group of healthy seniors, all 60 years of age or older. 22 subjects were randomized either to Gastrodia elata Blume powder form that was steeped in hot water or placebo. We measured the faculty of memory by using MMSE-K, Digit Span, Letter Fluency Test, Word List Memory Test, and Trail Making Test, and again after 12 weeks. Results: 1) Neither Gastrodia elata Blume groups nor control have a difference in MMSE-K, Digit Span, Letter Fluency Test, and Trail Making Test. 2) Gastrodia elata Blume group showed significant advances in immediate recall 1 and 2 of Word List Memory Test, and 3 g group show better results than the 4 g group. 3) 4 g Gastrodia elata Blume group showed significant advances in the recognition of Word List Memory Test. Conclusions: The results suggest that positive effects on memory improvement due to Gastrodia elata Blume depend on the amount.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1124-1133
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• 2000

Advanced Intelligent Network Intelligent Peripheral (AIN IP) is one of the AIN elements which consist of Service Control Point (SCP), Service Switching Point (SSP), and IP for AIN services, such as play announcement, digit collect, voice recognition/synthesis, voice prompt and receipt. This paper, featuring ISUP/INAP protocols, describes the procedures for call setup/release bearer channels between SSP/SCP and IP, todeliver specialized resources through the bearer channels, and it describes the structure and procedure for AIN services such as Automatic Collect Call (ACC), Universal Personal Telecommunication (UPT), and teleVOTing(VOT). In this environments, the delay characteristics of If system is investigated as the performance analysis, Policy establishment.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.78-85
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• 1999

In this paper we propose a method to improve the performance of the mel-cepstrum that is widely used in speech recognition. Typically, the met-cepstrum is obtained by critical band filters that have fixed center spacing and bandwidth. However different filter characteristics produce a different mel-cepstrum, resulting in a different performance. In this paper we analyze triangular-shaped and rectangular-shaped filters. By changing the characteristics of filters such as center frequency and bandwidth, we analyze the performance of the met-cepstrum. Then utilizing the simplex method, we propose a method to optimize the critical band filters. Using the dynamic time warping, we performed speaker independent recognition experiments with Korean digit words pronounced by 10 males and 10 females. Experiments show that the rectangular-shaped filters show good performance and the mel-cepstrum obtained by the optimized filters shows better performance than filters that have fixed center spacing and bandwidth.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.68-73
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• 2020

This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 ㎛ CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.