• 대한음성학회지:말소리
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• 제48호
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• pp.155-173
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• 2003

This paper proposes a noise reduction-based speech detection method under telephone channel environments. We adopt the AURORA front-end noise reduction algorithm based on the two-stage mel-warped Wiener filter approach as a preprocessor for the frequency domain speech detector. The speech detector utilizes mel filter-bank based useful band energies as its feature parameters. The preprocessor firstly removes the adverse noise components on the incoming noisy speech signals and the speech detector at the next stage detects proper speech regions for the noise-reduced speech signals. Experimental results show that the proposed noise reduction-based speech detection method is very effective in improving not only the performance of the speech detector but also that of the subsequent speech recognizer.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.145-148
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• 2003

To improve the recognition performance of Korean connected digit telephone speech, in this paper, both Aurora feature extraction method that employs noise reduction 2-state Wiener filter and DWFBA method are investigated and used. CMN and MRTCN are applied to static features for channel compensation. Telephone digit speech database released by SITEC is used for recognition experiments with HTK system. Experimental results has shown that Aurora feature is slightly better than MFCC and DWFBA without channel compensation. And when channel compensation is included, Aurora feature is slightly better than DWFBA with MRTCN.

• 한국전자통신학회논문지
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• 제9권5호
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• pp.543-548
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• 2014

다 모델 기반의 음성인식기는 음성인식에서 매우 성공적임이 알려져 있다. 그것은 다양한 신호-대-잡음비(SNR)와 잡음종류에 해당하는 다수의 HMM을 사용함으로서 선택된 음향모델이 인식잡음음성에 매우 근접한 일치성을 가질 수 있기 때문이다. 그러나 실제 사용시에 HMM의 개수가 제한됨에 따라서 음향모델의 불일치는 여전히 문제로 남아 있다. 본 논문에서는 인식잡음음성과 HMM 간의 SNR 불일치를 줄이고자 이들 간의 최적의 SNR 매핑 (mapping)을 실험적으로 결정하였다. 인식잡음음성으로 부터 추정된 SNR 값을 사용하는 대신 제안된 SNR 매핑을 사용함으로서 향상된 인식결과를 얻을 수 있었다. 다 모델 기반인식기에 제안된 방법을 적용하여 Aurora 2 데이터베이스에 대해서 인식 실험한 결과 기존의 MTR 이나 다 모델 기반 음성인식기에 비해서 6.3%와 9.4%의 상대적 단어 오인식율 감소를 이룰 수 있었다.

• ETRI Journal
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• 제32권5호
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• pp.801-809
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• 2010

This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decision-directed Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

• 대한음성학회지:말소리
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• 제60호
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• pp.181-190
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• 2006

This paper describes a speech enhancement scheme that leads to significant improvements in recognition performance when used in the ASR front-end. The proposed approach is based on adaptive comb filtering and an MMSE-related parameter estimator. While adaptive comb filtering reduces noise components remarkably, it is rarely effective in reducing non-stationary noises. Furthermore, due to the uniformly distributed frequency response of the comb-filter, it can cause serious distortion to clean speech signals. This paper proposes an improved comb-filter that adjusts its spectral magnitude to the original speech, based on the speech absence probability and the gain modification function. In addition, we introduce the modified comb filtering-based speech enhancement scheme for ASR in mobile environments. Evaluation experiments carried out using the Aurora 2 database demonstrate that the proposed method outperforms conventional adaptive comb filtering techniques in both clean and noisy environments.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.91-94
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• 2005

The performance of speech recognition is degraded by the mismatch between training and test environments. Many methods have been presented to compensate for additive noise and channel effect in the cepstral domain, and Cepstral Mean Subtraction (CMS) is the representative method among them. Recently, high order cepstral moment normalization method has introduced to improve recognition accuracy. In this paper, we apply high order moment normalization method and smoothing filter for real-time processing. In experiments using Aurora2 DB, we obtained error rate reduction of 49.7% with the proposed algorithm in comparison with baseline system.

• 말소리와 음성과학
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• 제9권2호
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• pp.103-110
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• 2017

The pole filtering concept has been successfully applied to cepstral feature normalization techniques for noise-robust speech recognition. In this paper, it is proposed to apply the pole filtering selectively only to the speech intervals, in order to further improve the recognition performance for short utterances in noisy environments. Experimental results on AURORA 2 task with clean-condition training show that the proposed selectively pole-filtered cepstral mean normalization (SPFCMN) and selectively pole-filtered cepstral mean and variance normalization (SPFCMVN) yield error rate reduction of 38.6% and 45.8%, respectively, compared to the baseline system.

• 대한음성학회지:말소리
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• 제54호
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• pp.63-72
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• 2005

The performance of speech recognition system is degraded by the mismatch between training and test environments. Many studies have been presented to compensate for noise components in the cepstral domain. Recently, higher order cepstral moment normalization method has been introduced to improve recognition accuracy. In this paper, we present real-time high order moment normalization method with post-processing smoothing filter to reduce the parameter estimation error in higher order moment computation. In experiments using Aurora2 database, we obtained error rate reduction of 44.7% with proposed algorithm in comparison with baseline system.