• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.147-150
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• 2005

The performance of speech recognition in car environment is severely degraded when there is music or news coming from a radio or a CD player. Since reference signals are available from the audio unit in the car, it is possible to remove them with an adaptive filter. In this paper, we present experimental results of speech recognition in car environment using the echo canceller. For this, we generate test speech signals by adding music or news to the car noisy speech from Aurora2 DB. The HTK-based continuous HMT system is constructed for a recognition system. In addition, the MMSE-STSA method is used to the output of the echo canceller to remove the residual noise more.

• Journal of Astronomy and Space Sciences
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• 제23권2호
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• pp.135-142
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• 2006

흑점수와 오로라의 관측 자료를 과거로 연장해 보면 dynamo 이론과 태양의 활동 그리고 기후간에 상당한 관계가 있음을 알 수 있다. 특히 흑점이 거의 나타나지 않았던 Maunder 극소기가 이상 혹한기였던 유럽의 소빙하기와 일치하고, 흑점의 출현과 오로라 발생 사이에 밀접한 관계가 있다는 사실이 알려졌다. 이에 따라 Maunder 극소기 시기의 흑점과 오로라의 관측 자료들을 간접적 solar proxy 자료들과 함께 조사하고, 이 시기에 나타난 태양 활동의 모습과 특징을 기후변화의 관계와 함께 알아보았다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1997년도 춘계학술대회 논문집
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• pp.338-341
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• 1997

According to reports, it is impossible to make Ohmic Contact with high resistivity p type CdTe or CdZnTe semiconductor theoretically. But it is in need of making Ohmic Contact to fabricate semiconductor radiation detector By electroless deposition method using gold chloride solution, we made Ohmic Contact of Au and p-Cd$_{80}$Zn$_{20}$Te which grown by High Presure Bridgman Method in Aurora Technologies Corporation. We investigated the interface with Rutherford Backscattering Spectrometry and Auger electron spectroscopy. And we evaluated the degree of Ohmic Contact for the Au/CdZnTe interface by the I/V characteristic curve. As a result, we concluded that it showed excellent Ohmic Contact property by tunneling mechanism through the interface.e.

• ETRI Journal
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• 제28권4호
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• 융합신호처리학회논문지
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• 제15권2호
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• pp.37-41
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• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• 말소리와 음성과학
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• 제6권2호
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• pp.29-34
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• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.515-516
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• 2018

본 논문은 의료 영상을 기반으로 중재시술을 위한 네비게이션 시스템을 제안한다. 네비게이션 시스템은 의료영상을 기반으로 로드맵을 제공하며, 병변지역까지의 최단경로를 A-start 알고리즘을 이용하여 네비게이션 서비스를 제공한다. 또한 카테터의 추적은 자기장 추적방법을 채택한 Aurora 시스템에 의해 실시간으로 모니터링 한다. 끝으로 뇌질환 팬텀을 통해 제안한 시스템의 제공하는 서비스 수행 결과를 보인다. 향후 수술 적용 범위를 넓혀 다양한 질환에 적용시키고자 한다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.59-62
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• 2005

Voice activity detection (VAD) is important in many areas of speech processing technology. Speech/nonspeech discrimination in noisy environments is a difficult task because the feature parameters used for the VAD are sensitive to the surrounding environments. Thus the VAD performance is severely degraded at low signal-to-noise ratios (SNRs). In this paper, a new VAD algorithm is proposed based on the degree of voicing and Quantile SNR (QSNR). These two feature parameters are more robust than other features such as energy and spectral entropy in noisy environments. The effectiveness of proposed algorithm is evaluated under the diverse noisy environments in the Aurora2 DB. According to out experiment, the proposed VAD outperforms the ETSI Advanced Frontend VAD.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
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• pp.39-42
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• 2004

본 논문에서는, 잡음음성에 강인한 음성인식을 위한 특징추출 방법을 제시한다. 제시한 방법은 2 단계 잡음제거 과정으로 구성되어 있다. 첫번째 단계는 MMSE-STSA 음성개선기법을 통해 잡음음성신호를 개선시키는 과정이고, 두 번째 단계는, MMSE-STSA 의 개선된 음성에 후처리 가중필터뱅크분석을 통해 잔여잡음의 영향을 감소시키는 과정이다. 제안한 방법의 성능평가를 위해, AURORA2의 잡음음성 DB 중 테스트 집합 A 에 대해 인식실험을 수행하고, 결과를 기존 방법들과 비교, 검토한다.

• 대한음성학회지:말소리
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• 제57호
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• pp.153-164
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• 2006

The MMSE-STSA based speech enhancement algorithm is widely used as a preprocessing for noise robust speech recognition. It weighs the gain of each spectral bin of the noisy speech using the estimate of noise and signal power spectrum. In this paper, we investigate the influence of parameters used to estimate the speech signal and noise power in MMSE-STSA upon the recognition performance of noisy speech. For experiments, we use the Aurora2 DB which contains noisy speech with subway, babble, car, and exhibition noises. The HTK-based continuous HMM system is constructed for recognition experiments. Experimental results are presented and discussed with our findings.