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Improving Speaker Recognition by Biometric Voice Deconstruction.

Mazaira-Fernandez LM, Álvarez-Marquina A, Gómez-Vilda P - Front Bioeng Biotechnol (2015)

Bottom Line: The present study benefits from the advances achieved during last years in understanding and modeling voice production.The paper hypothesizes that a gender-dependent characterization of speakers combined with the use of a set of features derived from the components, resulting from the deconstruction of the voice into its glottal source and vocal tract estimates, will enhance recognition rates when compared to classical approaches.Experimental validation is carried out both on a highly controlled acoustic condition database, and on a mobile phone network recorded under non-controlled acoustic conditions.

View Article: PubMed Central - PubMed

Affiliation: Neuromorphic Voice Processing Laboratory, Center for Biomedical Technology, Universidad Politécnica de Madrid , Madrid , Spain.

ABSTRACT
Person identification, especially in critical environments, has always been a subject of great interest. However, it has gained a new dimension in a world threatened by a new kind of terrorism that uses social networks (e.g., YouTube) to broadcast its message. In this new scenario, classical identification methods (such as fingerprints or face recognition) have been forcedly replaced by alternative biometric characteristics such as voice, as sometimes this is the only feature available. The present study benefits from the advances achieved during last years in understanding and modeling voice production. The paper hypothesizes that a gender-dependent characterization of speakers combined with the use of a set of features derived from the components, resulting from the deconstruction of the voice into its glottal source and vocal tract estimates, will enhance recognition rates when compared to classical approaches. A general description about the main hypothesis and the methodology followed to extract the gender-dependent extended biometric parameters is given. Experimental validation is carried out both on a highly controlled acoustic condition database, and on a mobile phone network recorded under non-controlled acoustic conditions.

No MeSH data available.


GMM–UBM speaker verification system. (A) UBM training. (B) GMM speaker model building. (C) Speaker Verification. (D) Score normalization.
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Figure 5: GMM–UBM speaker verification system. (A) UBM training. (B) GMM speaker model building. (C) Speaker Verification. (D) Score normalization.

Mentions: Although new modeling strategies have been proposed in recent years in order to improve recognition rates (Kinnunen and Li, 2010; Mazaira Fernández, 2014), the Gaussian mixture model (GMM)–universal background model (UBM) probabilistic paradigm strategy is still considered the de facto reference method in text-independent speaker recognition when the available amount of information for training purposes is limited. Figure 5 provides a block diagram of the speaker recognition system implemented applying the GMM–UBM approach. In this section, we do not care about the feature extraction process which has been already presented.


Improving Speaker Recognition by Biometric Voice Deconstruction.

Mazaira-Fernandez LM, Álvarez-Marquina A, Gómez-Vilda P - Front Bioeng Biotechnol (2015)

GMM–UBM speaker verification system. (A) UBM training. (B) GMM speaker model building. (C) Speaker Verification. (D) Score normalization.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585141&req=5

Figure 5: GMM–UBM speaker verification system. (A) UBM training. (B) GMM speaker model building. (C) Speaker Verification. (D) Score normalization.
Mentions: Although new modeling strategies have been proposed in recent years in order to improve recognition rates (Kinnunen and Li, 2010; Mazaira Fernández, 2014), the Gaussian mixture model (GMM)–universal background model (UBM) probabilistic paradigm strategy is still considered the de facto reference method in text-independent speaker recognition when the available amount of information for training purposes is limited. Figure 5 provides a block diagram of the speaker recognition system implemented applying the GMM–UBM approach. In this section, we do not care about the feature extraction process which has been already presented.

Bottom Line: The present study benefits from the advances achieved during last years in understanding and modeling voice production.The paper hypothesizes that a gender-dependent characterization of speakers combined with the use of a set of features derived from the components, resulting from the deconstruction of the voice into its glottal source and vocal tract estimates, will enhance recognition rates when compared to classical approaches.Experimental validation is carried out both on a highly controlled acoustic condition database, and on a mobile phone network recorded under non-controlled acoustic conditions.

View Article: PubMed Central - PubMed

Affiliation: Neuromorphic Voice Processing Laboratory, Center for Biomedical Technology, Universidad Politécnica de Madrid , Madrid , Spain.

ABSTRACT
Person identification, especially in critical environments, has always been a subject of great interest. However, it has gained a new dimension in a world threatened by a new kind of terrorism that uses social networks (e.g., YouTube) to broadcast its message. In this new scenario, classical identification methods (such as fingerprints or face recognition) have been forcedly replaced by alternative biometric characteristics such as voice, as sometimes this is the only feature available. The present study benefits from the advances achieved during last years in understanding and modeling voice production. The paper hypothesizes that a gender-dependent characterization of speakers combined with the use of a set of features derived from the components, resulting from the deconstruction of the voice into its glottal source and vocal tract estimates, will enhance recognition rates when compared to classical approaches. A general description about the main hypothesis and the methodology followed to extract the gender-dependent extended biometric parameters is given. Experimental validation is carried out both on a highly controlled acoustic condition database, and on a mobile phone network recorded under non-controlled acoustic conditions.

No MeSH data available.