Dealing with additive noise in speaker recognition systems based on i-vector approach

Matrouf, Driss; Kheder, Waad Ben; Bousquet, P.-M.; Ajili, Moez; Bonastre, J.-F.

doi:10.1109/eusipco.2015.7362753

Cited by 7 publications

(10 citation statements)

References 26 publications

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“…In our previous work [24][25][26], we proposed an additive noise model in the i-vector space represented by the equation:…”

Section: The Plda Model For I-vector Scoringmentioning

confidence: 99%

“…Where X and Y are two random variables representing respectively clean and noisy i-vectors and N represents the noise. Using full-covariance Gaussian distributions for both clean ivectors dX ∼ N (X; µX , ΣX ) and noise in the i-vector space dN ∼ N (N ; µN , ΣN ), it is possible to write the cleanedup versionX0 of a noisy i-vector Y0 using MAP criterion as [24][25][26]:…”

Section: The Plda Model For I-vector Scoringmentioning

confidence: 99%

“…In this paper, we explore two axes. On one side, we aim at improving the system performance by using two different techniques: 1 -The I-MAP algorithm [24][25][26] which is an i-vector denoising procedure based on an additive noise model in the i-vector space. It uses a Gaussian modeling of both clean i-vectors and the noise distributions in the i-vector space and have been proven to yield up to 60% of relative EER improvement compared to a baseline system performance.…”

Section: Introductionmentioning

confidence: 99%

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LIA System for the SITW Speaker Recognition Challenge

et al. 2016

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This paper presents the speaker verification systems developed in the LIA lab at the University of Avignon for the SITW (Speakers In The Wild) challenge. We present the algorithms used to deal with additive noise, short utterances and propose an improved scoring scheme using a discriminative classifier and integrating the homogeneity of the two compared recordings. Due to the heterogeneity of this database (presence of background noise, reverberation, Lombard effect, etc.), it is hard to analyze the contribution of individual techniques used to deal with each problem. For this reason, a subset of the trials will be studied for each algorithm in order to emphasize its contribution.

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“…In our previous work [24][25][26], we proposed an additive noise model in the i-vector space represented by the equation:…”

Section: The Plda Model For I-vector Scoringmentioning

confidence: 99%

Section: The Plda Model For I-vector Scoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LIA System for the SITW Speaker Recognition Challenge

et al. 2016

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“…In many established and emerging digital speech applications the effects of environmental noise has to be taken into considerations (Arowitz 2016, Matrouf et al 2015, Jiang et al 2017. This is because algorithms that perform well in a noise-free environment may degrade significantly in real-world environments where noise may be prevalent and unavoidable.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cancellation of Localised Environmental Noise

Noor¹,

Al-Hussaini²,

Samad³

2018

JKUKM

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Noise cancellation systems are useful in applications such as speech and speaker recognition systems where the effects of environmental noise have to be taken into considerations. A robust method for the cancellation of localised noise in noisy speech signals using subband decomposition and adaptive filtering is presented and described in this paper. The subband decomposition technique is based on low complexity octave filters that split the noisy speech input into subsidiary bands. A thresholding technique is then applied to the subbands to determine the presence or absence of environmental noise. This is used to control an adaptive filter which only responds to the noisy parts of the speech spectrum hence localising the adaptation process only on these segments. The Normalised Least Mean Squares algorithm (NLMS) is used for the adaptation process. A comparison with a similar system without localising the environmental noise shows the superior performance of the proposed system. It has been shown to perform better in terms of computational costs and convergence rate when compared to a system that does not take advantage of the information regarding the presence or absence of noise in a specific part of the speech spectrum. More than 35 dB of noise has been eliminated in less iterations than in conventional approach which needs longer time to reach steady state.

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“…We recently presented a new efficient Bayesian cleaning technique operating in the i-vector domain named I-MAP [20][21][22]. It is a "data-driven" i-vector cleaning method based on an additive noise model in the i-vector space.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Approach Using Joint Clean and Noisy i-Vectors Modeling for Speaker Recognition

et al. 2016

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Additive noise is one of the main challenges for automatic speaker recognition and several compensation techniques have been proposed to deal with this problem. In this paper, we present a new "data-driven" denoising technique operating in the i-vector space based on a joint modeling of clean and noisy i-vectors. The joint distribution is estimated using a large set of i-vectors pairs (clean i-vectors and their noisy versions generated artificially) then integrated in an MMSE estimator in the test phase to compute a "cleaned-up" version of noisy test ivectors. We show that this algorithm achieves up to 80% of relative improvement in EER. We also present a version of the proposed algorithm that can be used to compensate multiple "unseen" noises. We test this technique on the recently published SITW database and show a significant gain compared to the baseline system performance.

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Dealing with additive noise in speaker recognition systems based on i-vector approach

Cited by 7 publications

References 26 publications

LIA System for the SITW Speaker Recognition Challenge

LIA System for the SITW Speaker Recognition Challenge

Adaptive Cancellation of Localised Environmental Noise

Probabilistic Approach Using Joint Clean and Noisy i-Vectors Modeling for Speaker Recognition

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