• 대한음성학회지:말소리
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• 제63호
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• pp.125-137
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• 2007

Recently, many techniques have been proposed to improve the noise robustness for speaker verification. In this paper, we consider the feature recombination technique in multi-band approach. In the conventional feature recombination for speaker verification, to compute the likelihoods of speaker models or universal background model, whole feature components are used. This computation method is not effective in a view point of multi-band approach. To deal with non-effectiveness of the conventional feature recombination technique, we introduce a subband likelihood computation, and propose a modified feature recombination using subband likelihoods. In decision step of speaker verification system in noise environments, a few very low likelihood scores of a speaker model or universal background model cause speaker verification system to make wrong decision. To overcome this problem, a reliable feature selection method is proposed. The low likelihood scores of unreliable feature are substituted by likelihood scores of the adaptive noise model. In here, this adaptive noise model is estimated by maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. The proposed method using subband-based reliable feature selection obtains better performance than conventional feature recombination system. The error reduction rate is more than 31 % compared with the feature recombination-based speaker verification system.

• ETRI Journal
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• 제30권1호
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• pp.89-100
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• 2008

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.

• 한국음향학회지
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• 제30권3호
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• pp.149-157
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• 2011

본 논문은 support vector machine (SVM)을 사용하여 은닉 마코프 모델 (HMM)과 심잡음 존재 정보를 결합한 새로운 심장질환 분류 방법을 제안한다. 켑스트럼 특징과 HMM 비터비 (Viterbi) 알고리듬을 이용하여 입력 신호를 모든 심장질환 모델에 대하여 상태 단위로 분할하여 상태별로 로그우도 (점수)를 계산한다. 심잡음 신호의 시간적 위치 특성을 이용하기 위하여 입력신호를 두 개의 부대역으로 나누고 부대역별로 프레임 단위의 심잡음 점수를 계산한 다음, 비터비 알고리듬으로부터 구한 상태 분할 정보를 이용하여 상태단위의 심잡음 점수를 구한다. SVM은 모든 심장질환 종류에 대한 상태 단위의 HMM과 심잡음 점수를 입력으로 하여 최종적으로 심장질환을 판정한다. 심장질환 분류 실험결과, 제안한 방법은 기존의 켑스트럼 특징과 HMM 분류기를 이용한 방법에 비하여 20.4 %의 상대적 개선율을 보여준다.