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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.


DET curves comparing classical parameters in a gender-independent setup with the GDEB parameterization on MOBIO development set for male (left) and female (right) speakers.
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Figure 10: DET curves comparing classical parameters in a gender-independent setup with the GDEB parameterization on MOBIO development set for male (left) and female (right) speakers.

Mentions: The DET curves that represent the results obtained with each of the previously presented configurations are depicted in Figure 10 for male speakers (left) and for female speakers (right). Clearly, the proposed GDEB parameterization, in this case incorporating information just from the GSE in the form of MFCCs, is the configuration that provides the most successful results in the development set for both male and female speakers. The different tests carried out including the VTE parameters are worse than the results obtained using GSE parameters, but still improve recognition rates of GIC, as expected. Specifically, for the male speakers, the use of GSE setup, thus a gender-dependent configuration incorporating extended biometric features, provides a RR of 21% in terms of EERM, with respect to the GIC. Whereas in the case of female speakers, the use of the GSE setup allows for a relative reduction close to 11% in terms of EERF, with respect to the GIC.


Improving Speaker Recognition by Biometric Voice Deconstruction.

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

DET curves comparing classical parameters in a gender-independent setup with the GDEB parameterization on MOBIO development set for male (left) and female (right) speakers.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 10: DET curves comparing classical parameters in a gender-independent setup with the GDEB parameterization on MOBIO development set for male (left) and female (right) speakers.
Mentions: The DET curves that represent the results obtained with each of the previously presented configurations are depicted in Figure 10 for male speakers (left) and for female speakers (right). Clearly, the proposed GDEB parameterization, in this case incorporating information just from the GSE in the form of MFCCs, is the configuration that provides the most successful results in the development set for both male and female speakers. The different tests carried out including the VTE parameters are worse than the results obtained using GSE parameters, but still improve recognition rates of GIC, as expected. Specifically, for the male speakers, the use of GSE setup, thus a gender-dependent configuration incorporating extended biometric features, provides a RR of 21% in terms of EERM, with respect to the GIC. Whereas in the case of female speakers, the use of the GSE setup allows for a relative reduction close to 11% in terms of EERF, with respect to the GIC.

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.