• 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.

• 음성과학
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• 제13권2호
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• pp.75-84
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• 2006

Various methods have been proposed to overcome the problem of speech recognition in the noisy conditions. Among them, the model compensation methods like the parallel model combination (PMC) and Jacobian adaptation (JA) have been found to perform efficiently. The JA is quite effective when we have hidden Markov models (HMMs) already trained in a similar condition as the target environment. In a previous work, we have proposed an improved method for the JA to make it more robust against the changing environments in recognition. In this paper, we further improved its performance by compensating the delta-mean vectors and covariance matrices of the HMM and investigated its feasibility in the multi-model structure for the noisy speech recognition. From the experimental results, we could find that the proposed improved the robustness of the JA and the multi-model approach could be a viable solution in the noisy speech recognition.

• 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.

• 음성과학
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• 제11권2호
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• pp.247-257
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• 2004

There has been many research efforts to overcome the problems of speech recognition in the noisy conditions. Among them, the model-based compensation methods such as the parallel model combination (PMC) and vector Taylor series (VTS) have been found to perform efficiently compared with the previous speech enhancement methods or the feature-based approaches. In this paper, a data-driven model compensation approach that adapts the HMM(hidden Markv model) parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional model-based methods such as the PMC, the statistics necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.

• 대한음성학회지:말소리
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• 제62호
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• pp.85-96
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• 2007

In this paper, we carried out recognition experiments for noisy speech having various levels of car noise and output of an audio system using the speech interface. The speech interface consists of three parts: pre-processing, acoustic echo canceller, post-processing. First, a high pass filter is employed as a pre-processing part to remove some engine noises. Then, an echo canceller implemented by using an FIR-type filter with an NLMS adaptive algorithm is used to remove the music or speech coming from the audio system in a car. As a last part, the MMSE-STSA based speech enhancement method is applied to the out of the echo canceller to remove the residual noise further. For recognition experiments, we generated test signals by adding music to the car noisy speech from Aurora 2 database. The HTK-based continuous HMM system is constructed for a recognition system. Experimental results show that the proposed speech interface is very promising for robust speech recognition in a noisy car environment.

• 한국음향학회지
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• 제20권2호
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• pp.21-26
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• 2001

본 논문에서는 잡음에 강인한 음성인식을 위해서 expectation-maximization (EM) 방식을 이용하여 잡음의 평균값을 추정하는 새로운 알고리듬을 제안하였다. 제안된 알고리듬에서는 온라인상의 인식용 음성이 직접 Bayesian 적응을 위해서 사용되며, 또한 훈련데이터를 이용하여 잡음의 평균값에 대한 사전 (prior) 분포를 알아낸 후 Bayesian 적응시에 이용한다. 잡음 음성의 모델링을 위해서는 PMC (parallel model combination) 방식을 이용하였고, 제안된 방식을 이용하여 자동차 잡음 환경 하에서 인식 실험을 수행한 결과, 기존의 PMC 방식에 비해서 향상된 인식성능을 보임을 알 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.738-741
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• 2009

본 연구는 잡음을 포함한 음성 환경에서의 음성인식을 개선방안에 관한 것이다. 우리는 음성인식에서 잡음 섞인 음성으로부터 얻은 스펙트럴 envelope에서 곡들의 스펙트럴 subtraction 및 복원이 보다 더 효과적임을 알 수 있었다. 본 연구에서, 평균화된 스펙트럴 envelope은 모음 스펙트럼으로부터 추출하여 곡들의 강조에 사용하였다. 낮은 주파수 영역에서의 모음 스펙트럴 정보는 강조되어지고 자음으로부터 얻은 스펙트럼은 변하지 않는다. 시뮬레이션으로 살펴보면, 강조계수는 켑스트럴 영역에서 변한다. 이 방법으로 잡음석인 숫자음성 인식에서 적용하였으며 인식결과가 개선됨을 알 수 있었다.

• 음성과학
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• 제14권2호
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• pp.115-126
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• 2007

There have been many research efforts to improve the performance of the speech recognizer in noisy conditions. Among them, the model compensation method and the speech enhancement approach have been used widely. In this paper, we propose to combine the two different approaches to further enhance the recognition rates in the noisy speech recognition. For the speech enhancement, the minimum mean square error-short time spectral amplitude (MMSE-STSA) has been adopted and the parallel model combination (PMC) and Jacobian adaptation (JA) have been used as the model compensation approaches. From the experimental results, we could find that the hybrid approach that applies the model compensation methods to the enhanced speech produce better results than just using only one of the two approaches.

• 말소리와 음성과학
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• 제1권2호
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• pp.9-14
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• 2009

Speech enhancement methods can be generally classified into a few categories and they have been usually compared with each other in terms of speech quality. For the successful use of speech enhancement methods in speech recognition systems, performance comparisons in terms of speech recognition accuracy are necessary. In this paper, we compared the speech recognition performance of some of the representative speech enhancement algorithms which are popularly cited in the literature and used widely. We also compared the performance of speech enhancement methods with other noise robust speech recognition methods like PMC to verify the usefulness of speech enhancement approaches in noise robust speech recognition systems.