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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.


Related in: MedlinePlus

Separation algorithm using first-order prediction lattice and including a lip-radiation compensation stage.
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Figure 1: Separation algorithm using first-order prediction lattice and including a lip-radiation compensation stage.

Mentions: While a comprehensive description can be found in Mazaira Fernández (2014), Figure 1 provides the block diagram of the voice deconstruction algorithm used to separate the vocal tract and glottal estimates of voice from continuous speech. The proposed algorithm not only allows for a simultaneous estimation of both voice components, but also guarantees that they are orthogonal in terms of correlation. A brief description of the main blocks involved in the process is given below:


Improving Speaker Recognition by Biometric Voice Deconstruction.

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

Separation algorithm using first-order prediction lattice and including a lip-radiation compensation stage.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Separation algorithm using first-order prediction lattice and including a lip-radiation compensation stage.
Mentions: While a comprehensive description can be found in Mazaira Fernández (2014), Figure 1 provides the block diagram of the voice deconstruction algorithm used to separate the vocal tract and glottal estimates of voice from continuous speech. The proposed algorithm not only allows for a simultaneous estimation of both voice components, but also guarantees that they are orthogonal in terms of correlation. A brief description of the main blocks involved in the process is given below:

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.


Related in: MedlinePlus