On speech features fusion, α-integration Gaussian modeling and multi-style training for noise robust speaker classification

Venturini, A.; Zão, L.; Coelho, R.

doi:10.1109/taslp.2014.2355821

Cited by 24 publications

(18 citation statements)

References 36 publications

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“…In the area of GMM-based speaker [14] and emotional voice classification [15] analysis in our experiment arises from the previous research [16,17]. First, the weighted frames of the input sentence are used to calculate the energy contour from the first cepstral coefficient.…”

Section: Determination Of Speech Featuresmentioning

confidence: 99%

GMM-based speaker age and gender classification in Czech and Slovak

Přibil

Přibilová

Matoušek

2017

Journal of Electrical Engineering

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The paper describes an experiment with using the Gaussian mixture models (GMM) for automatic classification of the speaker age and gender. It analyses and compares the influence of different number of mixtures and different types of speech features used for GMM gender/age classification. Dependence of the computational complexity on the number of used mixtures is also analysed. Finally, the GMM classification accuracy is compared with the output of the conventional listening tests. The results of these objective and subjective evaluations are in correspondence.K e y w o r d s: GMM classifier, spectral and prosodic features of speech, speaker gender and age classification

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Section: Determination Of Speech Featuresmentioning

confidence: 99%

GMM-based speaker age and gender classification in Czech and Slovak

Přibil

Přibilová

Matoušek

2017

Journal of Electrical Engineering

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“…In the area of the GMM-based speaker [28]- [30], as well as the acoustic signal recognition [31], the most commonly used spectral features are mel-frequency cepstral coefficients together with energy and prosodic parameters. In our experiments the features differ for ANOVA-based evaluation and GMM-based classification of the speech signal quality.…”

Section: Determination Of Features Of the Speech Signalmentioning

confidence: 99%

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

Přibil

Přibilová²,

Frollo

2017

Measurement Science Review

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The paper focuses on two methods of evaluation of successfulness of speech signal enhancement recorded in the open-air magnetic resonance imager during phonation for the 3D human vocal tract modeling. The first approach enables to obtain a comparison based on statistical analysis by ANOVA and hypothesis tests. The second method is based on classification by Gaussian mixture models (GMM). The performed experiments have confirmed that the proposed ANOVA and GMM classifiers for automatic evaluation of the speech quality are functional and produce fully comparable results with the standard evaluation based on the listening test method.

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“…Coloured, white, and narrow-band noises are applied to alpha-GMM classifier for multi-style training [30]. The goal of the multi-style training is to reduce the mismatch between training and test features by corrupting the speech signals.…”

Section: Robust Features Against Additive Noisementioning

confidence: 99%

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

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Speech is a signal that includes speaker's emotion, characteristic specification, phonemeinformation etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.

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On speech features fusion, α-integration Gaussian modeling and multi-style training for noise robust speaker classification

Cited by 24 publications

References 36 publications

GMM-based speaker age and gender classification in Czech and Slovak

GMM-based speaker age and gender classification in Czech and Slovak

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

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