Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison

Walawalkar, L.; Yeasin, Mohammad; Narasimhamurthy, Anand; Sharma, Rajeev

doi:10.1007/3-540-45665-1_12

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…Indeed, automatic sex recognition in normophonic voice has been discussed before in the literature. In [24] the authors employ cepstral features and support vector machines (SVM) for sex recognition, obtaining 100% classification accuracy when using English allophones as acoustic material. In [25] the authors develop a methodology based on relative spectral perceptual linear predictive coefficients and Gaussian mixture models (GMM).…”

Section: Introductionmentioning

confidence: 99%

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

García

Moro-Velázquez

Godino-Llorente

et al. 2016

Rev. Fac. Ing. Antioquia

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An automatic categorization of the speakers according to their sex improves the performance of an automatic detector of voice pathologies. This is grounded on findings demonstrating perceptual, acoustical and anatomical differences in males' and females' voices. In particular, this paper follows two objectives: 1) to design a system which automatically discriminates the sex of a speaker when using normophonic and pathological speech, 2) to study the influence that this sex detector has on the accuracy of a further voice pathology detector. The parameterization of the automatic sex detector relies on MFCC applied to speech; and MFCC applied to glottal waveforms plus parameters modeling the vocal tract. The glottal waveforms are extracted from speech via iterative lattice inverse filters. Regarding the pathology detector, a MFCC parameterization is applied to speech signals. Classification, in both sex and pathology detectors, is carried out using state of the art techniques based on universal background models. Experiments are performed in the Saarbrücken database, employing the sustained phonation of vowel /a/. Results indicate that the sex of the speaker may be discriminated automatically using normophonic and pathological speech, obtaining accuracy up to 95%. Moreover, including the a-priori information about the sex of the speaker produces an absolute performance improvement in EER of about 2% on pathology detection tasks. RESUMEN:Una categorización automática de los hablantes de acuerdo con su sexo mejora el rendimiento de un detector automático de patologías de voz. Esto se fundamenta en hallazgos que demuestran diferencias perceptuales, acústicas y anatómicas en voces masculinas y femeninas. En particular, este trabajo persigue dos objetivos: 1) diseñar un sistema que discrimine automáticamente el sexo de hablantes utilizando habla normofónica y patológica, 2) estudiar la influencia que este detector de sexo tiene sobre el acierto de un posterior detector de patologías de voz. La parametrización del detector automático de sexo se basa en MFCC aplicados sobre señales de voz; y MFCC aplicados a formas de onda glotal junto a parámetros que modelan el tracto vocal. Las formas de onda glotal se extraen de la voz a través de un filtrado inverso iterativo en celosía. En cuanto al detector de patologías, una parametrización MFCC se aplica a señales de voz. La clasificación, tanto en los detectores de sexo como de patología, se lleva a cabo con técnicas del estado del arte basadas en modelos de base universal. Experimentos son realizados sobre la base de datos Saarbrücken empleando la fonación sostenida de la vocal /a/. Los resultados indican que el sexo del hablante puede ser discriminado automáticamente utilizando habla normofónica y

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Section: Introductionmentioning

confidence: 99%

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

García

Moro-Velázquez

Godino-Llorente

et al. 2016

Rev. Fac. Ing. Antioquia

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show abstract

“…Although some that standard voice modeling approaches ca accurate results for gender classificatio conditions [13] [14], there has been little r robustness of these approaches when op conditions. Various approaches have been past to improve the robustness of speake noisy conditions and it is feasible that t would provide improved robustness for g [15].…”

Section: Introductionmentioning

confidence: 99%

Investigations into the Robustness of Audio-Visual Gender Classification to Background Noise and Illumination Effects

Stewart

Wang

Shen

et al. 2009

2009 Digital Image Computing: Techniques and Applications

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“…SVM can be regarded as one of the successful techniques for classification [18] [19]. SVM is an optimal discriminant method based on Bayesian learning theory.…”

Section: Support Vector Machinementioning

confidence: 99%

Support Vector Machine vs Pattern Network for Gait Recognition

AbdAlKader

2017

IJARCSSE

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Abstract-Human recognition based on biometric information is important due to its reliability in identity verification. Gait recognition has ability to recognize individuals from a distance. This study includes human gait recognition based firstly on support vector machine (SVM) and secondly on PatternNet neural network. Experiments were conducted with comparisons based on the two approaches. Experimental results showed that the PattenNet neural network is more effective than the SVM in gait recognition.

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Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison

Cited by 17 publications

References 15 publications

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

Investigations into the Robustness of Audio-Visual Gender Classification to Background Noise and Illumination Effects

Support Vector Machine vs Pattern Network for Gait Recognition

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