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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.


Power spectral density of the glottal source evaluated over a temporal window which includes multiple glottal cycles. The relative maxima of the distribution are marked by the harmonics present in the signal. The interconnection of these maxima is known as harmonic envelope or power spectral density profile.
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Figure 3: Power spectral density of the glottal source evaluated over a temporal window which includes multiple glottal cycles. The relative maxima of the distribution are marked by the harmonics present in the signal. The interconnection of these maxima is known as harmonic envelope or power spectral density profile.

Mentions: Figure 2 depicts the VTE and GSE obtained from a female speech utterance of vowel/a/applying the described algorithm. Additionally, Figure 3 represents the power spectral density of the GSE, evaluated over a temporal window which includes multiple glottal cycles. This figure clearly shows a peak and trough patterns, agreeing with previous works in the area (Gómez Vilda et al., 2009, 2013; Mazaira Fernández, 2014) that the glottal source do not present a flat spectrum.


Improving Speaker Recognition by Biometric Voice Deconstruction.

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

Power spectral density of the glottal source evaluated over a temporal window which includes multiple glottal cycles. The relative maxima of the distribution are marked by the harmonics present in the signal. The interconnection of these maxima is known as harmonic envelope or power spectral density profile.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Power spectral density of the glottal source evaluated over a temporal window which includes multiple glottal cycles. The relative maxima of the distribution are marked by the harmonics present in the signal. The interconnection of these maxima is known as harmonic envelope or power spectral density profile.
Mentions: Figure 2 depicts the VTE and GSE obtained from a female speech utterance of vowel/a/applying the described algorithm. Additionally, Figure 3 represents the power spectral density of the GSE, evaluated over a temporal window which includes multiple glottal cycles. This figure clearly shows a peak and trough patterns, agreeing with previous works in the area (Gómez Vilda et al., 2009, 2013; Mazaira Fernández, 2014) that the glottal source do not present a flat spectrum.

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.