• Title/Summary/Keyword: Speaker Identification

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Confidence Measure of Forensic Speaker Identification System According to Pitch Variances (과학수사용 화자 식별 시스템의 피치 차이에 따른 신뢰성 척도)

  • Kim, Min-Seok;Kim, Kyung-Wha;Yang, IL-Ho;Yu, Ha-Jin
    • Phonetics and Speech Sciences
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    • v.2 no.3
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    • pp.135-139
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    • 2010
  • Forensic speaker identification needs high accuracy and reliability. However, the current level of speaker identification does not reach its demand. Therefore, the confidence evaluation of results is one of the issues in forensic speaker identification. In this paper, we propose a new confidence measure of forensic speaker identification system. This is based on pitch differences between the registered utterances of the identified speaker and the test utterance. In the experiments, we evaluate this confidence measure by speech identification tasks on various environments. As the results, the proposed measure can be a good measure indicating if the result is reliable or not.

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Performance Enhancement of Speaker Identification System Based on GMM Using the Modified EM Algorithm (수정된 EM알고리즘을 이용한 GMM 화자식별 시스템의 성능향상)

  • Kim, Seong-Jong;Chung, Ik-Joo
    • Speech Sciences
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    • v.12 no.4
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    • pp.31-42
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    • 2005
  • Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

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Speaker Identification using Phonetic GMM (음소별 GMM을 이용한 화자식별)

  • Kwon Sukbong;Kim Hoi-Rin
    • Proceedings of the KSPS conference
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    • pp.185-188
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    • 2003
  • In this paper, we construct phonetic GMM for text-independent speaker identification system. The basic idea is to combine of the advantages of baseline GMM and HMM. GMM is more proper for text-independent speaker identification system. In text-dependent system, HMM do work better. Phonetic GMM represents more sophistgate text-dependent speaker model based on text-independent speaker model. In speaker identification system, phonetic GMM using HMM-based speaker-independent phoneme recognition results in better performance than baseline GMM. In addition to the method, N-best recognition algorithm used to decrease the computation complexity and to be applicable to new speakers.

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GMM based Speaker Identification using Pitch Information (피치 정보를 이용한 GMM 기반의 화자 식별)

  • Park Taesun;Hahn Minsoo
    • MALSORI
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    • no.47
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    • pp.121-129
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    • 2003
  • This paper describes the use of pitch information for speaker identification. The recognition system is a GMM based one with 4 connected Korean digits speech database. The mean of the pitch period in voiced sections of speech are shown to be ,useful at discriminating between speakers. Utilizing this feature with Gaussian mixture model in the speaker identification system gave a marked improvement, maximum 6% improvement comparing to the baseline Gaussian mixture model.

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A Study on Speaker Identification Parameter Using Difference and Correlation Coeffieicent of Digit_sound Spectrum (숫자음의 스펙트럼 차이값과 상관계수를 이용한 화자인증 파라미터 연구)

  • Lee, Hoo-Dong;Kang, Sun-Mee;Chang, Moon-Soo;Yang, Byung-Gon
    • Speech Sciences
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    • v.11 no.3
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    • pp.131-142
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    • 2004
  • Speaker identification system basically functions by comparing spectral energy of an individual production model with that of an input signal. This study aimed to develop a new speaker identification system from two parameters from the spectral energy of numeric sounds: difference sum and correlation coefficient. A narrow-band spectrogram yielded more stable spectral energy across time than a wide-band one. In this paper, we collected empirical data from four male speakers and tested the speaker identification system. The subjects produced 18 combinations of three-digit numeric. sounds !en times each. Five productions of each three-digit number were statistically averaged to make a model for each speaker. Then, the remaining five productions were tested on the system. Results showed that when the threshold for the absolute difference sum was set to 1200, all the speakers could not pass the system while everybody could pass if set to 2800. The minimum correlation coefficient to allow all to pass was 0.82 while the coefficient of 0.95 rejected all. Thus, both threshold levels can be adjusted to the need of speaker identification system, which is desirable for further study.

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Speaker Identification Based on Vowel Classification and Vector Quantization (모음 인식과 벡터 양자화를 이용한 화자 인식)

  • Lim, Chang-Heon;Lee, Hwang-Soo;Un, Chong-Kwan
    • The Journal of the Acoustical Society of Korea
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    • v.8 no.4
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    • pp.65-73
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    • 1989
  • In this paper, we propose a text-independent speaker identification algorithm based on VQ(vector quantization) and vowel classification, and its performance is studied and compared with that of a conventional speaker identification algorithm using VQ. The proposed speaker identification algorithm is composed of three processes: vowel segmentation, vowel recognition and average distortion calculation. The vowel segmentation is performed automatlcally using RMS energy, BTR(Back-to-Total cavity volume Ratio)and SFBR(Signed Front-to-Back maximum area Ratio) extracted from input speech signal. If the Input speech signal Is noisy, particularity when the SNR is around 20dB, the proposed speaker identification algorithm performs better than the reference speaker identification algorithm when the correct vowel segmentation is done. The same result is obtained when we use the noisy telephone speech signal as an input, too.

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Text-Independent Speaker Identification System Based On Vowel And Incremental Learning Neural Networks

  • Heo, Kwang-Seung;Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1042-1045
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    • 2003
  • In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

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Speaker Identification Using GMM Based on Local Fuzzy PCA (국부 퍼지 클러스터링 PCA를 갖는 GMM을 이용한 화자 식별)

  • Lee, Ki-Yong
    • Speech Sciences
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    • v.10 no.4
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    • pp.159-166
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    • 2003
  • To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix in each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method needs less storage and shows faster result, under the same performance.

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Statistical Extraction of Speech Features Using Independent Component Analysis and Its Application to Speaker Identification

  • Jang, Gil-Jin;Oh, Yung-Hwan
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.4E
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    • pp.156-163
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    • 2002
  • We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

A Robust Speaker Identification Using Optimized Confidence and Modified HMM Decoder (최적화된 관측 신뢰도와 변형된 HMM 디코더를 이용한 잡음에 강인한 화자식별 시스템)

  • Tariquzzaman, Md.;Kim, Jin-Young;Na, Seung-Yu
    • MALSORI
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    • no.64
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    • pp.121-135
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    • 2007
  • Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

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