• 말소리와 음성과학
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• 제4권3호
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• pp.95-101
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• 2012

In this paper, we introduce the automatic speaker identification system 'SPO(Supreme Prosecutors Office) Verifier'. SPO Verifier is a GMM(Gaussian mixture model)-UBM(universal background model) based automatic speaker recognition system and has been developed using Korean speakers' utterances. This system uses a channel compensation algorithm to compensate recording device characteristics. The system can give the users the ability to manage reference models with utterances from various environments to get more accurate recognition results. To evaluate the performance of SPO Verifier on Korean speakers, we compared this system with one of the most widely used commercial systems in the forensic field. The results showed that SPO Verifier shows lower EER(equal error rate) than that of the commercial system.

• 말소리와 음성과학
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• 제4권2호
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• pp.79-86
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• 2012

In this paper, we propose a novel approach to improve histogram equalization for speaker identification. Our method collects all speech features of UBM training data to make a reference distribution. The ranks of the feature vectors are calculated in the sorted list of the collection of the UBM training data and the test data. We use the ranks to perform order-based histogram equalization. The proposed method improves the accuracy of the speaker recognition system with short utterances. We use four kinds of speech databases to evaluate the proposed speaker recognition system and compare the system with cepstral mean normalization (CMN), mean and variance normalization (MVN), and histogram equalization (HEQ). Our system reduced the relative error rate by 33.3% from the baseline system.

• ETRI Journal
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• 제40권5호
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• pp.643-652
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• 2018

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature-extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well-known Mel-frequency cepstral coefficient features. For classification purposes, the use of a multi-layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text-dependent and text-independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.

• 한국음향학회지
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• 제21권8호
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• pp.735-743
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• 2002

본 논문에서는 세 가지 문맥독립 화자식별방법을 제안한다. 먼저, 화자 식별시 성도의 특성을 충분히 표현하지 못한 프레임이 포함되지 않도록 하는 프레임선택 (Frame Selection; FS)방법을 제안한다. 이 방법은 각 프레임에서 가장 큰 유사도와 두 번째로 큰 유사도의 차이를 평가하여 중요 프레임을 선택한 후, 선택된 프레임만을 이용하여 유사도를 계산하는 방법이다. 두 번째로 제안하는 복합 (Hyrid)방법은 FS와 가중모델순위 (Weighting Model Rank: WMR)방법을 결합시킨 것으로, FS방법을 이용하여 중요 프레임을 선택한 후, 지수함수 가중치를 이용하여 식별화자를 결정하는 것이다. 마지막으로 제안하는 수정된 가중모델순위 (Modified WMR; MWMR)방법은 식별화자를 결정할 때 유사도의 상대적 위치만을 고려하였던 기존의 U방법과는 달리 유사도와 유사도의 상대적 위치를 함께 고려하는 방법이다. 화자식별 실험결과 제안한 방법들이 기존의 ML 방법보다 향상된 식별률을 보였으며, 복합 방법 및 MWMR방법의 경우에는 WMR방법보다 각각 약 2%와 3%의 향상된 식별률을 나타내어 제안한 방법들의 유효성을 확인할 수 있었다.

• 한국지능시스템학회논문지
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• 제8권2호
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• pp.82-95
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• 1998

본 논문에서는 구문독립 화지인식 시스템에서 가장 중요한 역할을 하는 분류기를 두 단계로 나누어, 먼저 짧은 구간들에 대해서 각각의 화자에 속하는 정도를 계산하고, 다음에 계산된 결과들을 가지고 주어진 음성구간전체에 대해 가장 가능성이 높은 화자를 선택하는 구조를 제안한다. 첫번째 부분은 학습에 의해 스스로 조기하는 RBFN을 이용하여 구현하고 두번째 부분에서는 MAXNET과 지연합의 조합으로 화자를 결정한다. 이렇게 함으로써 지연합의 개수가 증가함에 따라 인식률이 100%가 되는 것을 모의 실험을 통하여 확인한다. 또한 본 논문에서는 음성의 프랙탈적인 특징이 화자인식에 사용될 수 있는지를 검토한다. 화자인식은 동질의 집단에서 13명의 성인만자의 목소리를 이용하여 닫힌집합(closed-set)의 경우로 모의실험을 하였고, 기존의 특징으로는 선형예측계수(LPC) 와 PC-cepstrum을 사용하였다.

• 대한음성학회지:말소리
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• 제62호
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• pp.149-161
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• 2007

In this paper we proposed a modified reliability function for improving bimodal speaker identification(BSI) performance. The convectional reliability function, used by N. Fox[1], is extended by introducing an optimization factor. We evaluated the proposed method in BSI domain. A BSI system was implemented based on GMM and it was tested using VidTIMIT database. Through speaker identification experiments we verified the usefulness of our proposed method. The experiments showed the improved performance, i.e., the reduction of error rate by 39%.

• 음성과학
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• 제14권4호
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• pp.145-156
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• 2007

The performance of speaker identification is severely degraded in noisy environments. Recently probability weighting method based on observation membership was proposed for overcoming the noise problem[1]. In the paper[1] the observation confidence was calculated from SNR with sigmoid function. However, estimating SNR needs additive calculation amount and estimated SNR is corrupted in dynamic noisy environments. In this paper we propose estimation methods of the observation confidence based on score-based reliabilities (SBR) of entropy and dispersion measures. Generally SBRs are obtained from speaker models' probabilities. The proposed methods are evaluated with ETRI speaker recognition DB. We compared the performances of the proposed methods with those in [1][8]. The experimental results show that the proposed methods can be successfully applied for the case where SNR is not available.

• The Journal of the Acoustical Society of Korea
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• 제26권3E호
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• pp.80-86
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• 2007

In this paper, we utilize generalized warped filterbanks to improve the performance of speaker recognition systems. At first, the performance of speaker identification systems is analyzed by varying the type of warped filterbanks. Based on the results that the error pattern of recognition system is different depending on the type of filterbank used, we combine the likelihood values of the statistical models that consist of the features extracting from multiple warped filterbanks. Simulation results with TIMIT and NTIMIT database verify that the proposed system shows relative improvement of identification rate by 31.47% and 15.14% comparing it to the conventional system.

• 대한음성학회지:말소리
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• 제66호
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• 음성과학
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• 제2권
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• pp.57-66
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• 1997

This paper studies the effects of the method, CMS(Cepstral Mean Subtraction), (which compensates for some of the speech distortion. caused by telephone channels), on the performance of the text-dependent speaker identification system. This system is based on the VQ(Vector Quantization) and HMM(Hidden Markov Model) method and chooses the LPC-Cepstrum and Mel-Cepstrum as the feature vectors extracted from the speech data transmitted through telephone channels. Accordingly, we can compare the correct recognition rates of the speaker identification system between the use of LPC-Cepstrum and Mel-Cepstrum. Finally, from the experiment results table, it is found that the Mel-Cepstrum parameter is proven to be superior to the LPC-Cepstrum and that recognition performance improves by about 10% when compensating for telephone channel using the CMS.