A Support Vector Machine-Based Gender Identification Using Speech Signal

Lee, Kye Hwan; Kang, Sang Ick; Kim, Deok Hwan; Chang, Joon‐Hyuk

doi:10.1093/ietcom/e91-b.10.3326

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…2, the receiver operating characteristic (ROC) curves, which show the trade-off between the reduction of P fa (false-alarm probability) and the increase in detection probability (P d ), are displayed for all test files. From the results, we can see that the proposed algorithm outperformed the previous method [6] in discriminating gender at the relevant intervals. The test results confirm that the optimally weighted MFCC improves the performance of gender identification in actual conditions.…”

Section: Resultsmentioning

confidence: 88%

“…In this section, we briefly review the notion of the support vector machine incorporating the MFCCs used for gender identification [6]. At first, let…”

Section: Support Vector Machine-based Gender Identification Employingmentioning

confidence: 99%

“…One interesting insight pertaining to the MFCC-based SVM scheme for gender identification is that no weights are assigned to the MFCC [6,7]. To incorporate the different contributions of the MFCC in discriminating gender from human voice, we propose a technique adopting various weights for the MFCC such as w k c k .…”

Section: Mfcc Weight Optimization Using Mce Trainingmentioning

confidence: 99%

“…Especially, Tyagi and Wellekens suggested a method to desensitize the MFCC coefficients to spurious low-energy spectral perturbation and reported enhanced performance for speech recognition [5]. In this regard, it has been observed in the literature that no weights are applied to the MFCCs in all mel-filter bank indexes without taking full consideration of the relative importance of the MFCCs for gender identification [6,7]. Accordingly, in this paper, we present a novel way for gender identification to incorporate optimally weighted MFCCs based on a minimum error classification (MCE) scheme, an approach that is well known as an effective discriminative weight training [8].…”

Section: Introductionmentioning

confidence: 99%

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Discriminative weight training-based optimally weighted MFCC for gender identification

Kang¹,

Chang²

2009

IEICE Electron. Express

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Abstract:In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differently assigned to each MFCC which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification based on the SVM. Keywords: gender identification, MFCC, MCE Classification: Science and engineering for electronics References[1] C. Neti and S. Roukos, "Phone-context specific gender-dependent acoustic-models for continuous speech recognition," Proc. IEEE Autom. Speech Recognition Understanding Workshop, pp. 192-198, Dec. 1997. [2] D. F. Marston, "Gender adapted speech coding," Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 1, pp. 357-360, 12-15, May 1998. [3] H. Harb and L. Chen, "Voice-based gender identification in multimedia applications," J. Intell. Inf. Syst., vol. 24,

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Section: Resultsmentioning

confidence: 88%

“…In this section, we briefly review the notion of the support vector machine incorporating the MFCCs used for gender identification [6]. At first, let…”

Section: Support Vector Machine-based Gender Identification Employingmentioning

confidence: 99%

Section: Mfcc Weight Optimization Using Mce Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminative weight training-based optimally weighted MFCC for gender identification

Kang¹,

Chang²

2009

IEICE Electron. Express

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“…Some other classifiers have also been used. For 1 -Automatic Speech Recognition 2 -Hidden Markov Model example Lee and Lang (2008) used SVM 3 [6] and Silvosky and Nouza (2006) used GMM 4 [7].…”

Section: Introductionmentioning

confidence: 99%

Performance improvement in automatic gender identification using hierarchical clustering

Keyvanrad

Homayounpour

2010

2010 5th International Symposium on Telecommunications

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In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature selection to reduce clustering and classification run time. Another idea used in this paper is to choose and use the best classifier in each level of the proposed hierarchical gender classifier. Three classifiers including GMM, SVM and MLP are used. 96.19% gender classification accuracy was obtained on OGI Multilanguage Telephony Corpus which is 3.5%, 2.5%, and 1% better than the performance obtained by traditional classifiers using GMM, SVM and MLP respectively.

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Multiple Order Gradient Feature for Macro-Invertebrate Identification Using Support Vector Machines

Tirronen

Caponio

Haanpää

et al. 2009

Lecture Notes in Computer Science

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A Support Vector Machine-Based Gender Identification Using Speech Signal

Cited by 19 publications

References 11 publications

Discriminative weight training-based optimally weighted MFCC for gender identification

Discriminative weight training-based optimally weighted MFCC for gender identification

Performance improvement in automatic gender identification using hierarchical clustering

Multiple Order Gradient Feature for Macro-Invertebrate Identification Using Support Vector Machines

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