The effect of noise on modern automatic speaker recognition systems

Mandasari, Miranti Indar; McLaren, Mitchell; Leeuwen, David A. van

doi:10.1109/icassp.2012.6288857

Cited by 41 publications

(20 citation statements)

References 7 publications

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“…Varying utterance duration was compensated for by calibrating the PLDA score in (Hasan et al, 2013), and by exploiting the uncertainty in the i-vector in (Cumani et al, 2013b). The effect of noise on PLDA-based systems is studied in (Mandasari et al, 2012). The experimental protocol in most of the above mentioned works involved a single i-vector for enrollment.…”

Section: Introductionmentioning

confidence: 99%

From single to multiple enrollment i-vectors: Practical PLDA scoring variants for speaker verification

Rajan

Afanasyev

Hautamäki

et al. 2014

Digital Signal Processing

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The availability of multiple utterances (and hence, i-vectors) for speaker enrollment brings up several alternatives for their utilization with probabilistic linear discriminant analysis (PLDA). This paper provides an overview of their effective utilization, from a practical viewpoint. We derive expressions for the evaluation of the likelihood ratio for the multi-enrollment case, with details on the computation of the required matrix inversions and determinants. The performance of five different scoring methods, and the effect of i-vector length normalization is compared experimentally. We conclude that length normalization is a useful technique for all but one of the scoring methods considered, and averaging i-vectors is the most effective out of the methods compared. We also study the application of multicondition training on the PLDA model. Our experiments indicate that multicondition training is more effective in estimating PLDA hyperparameters than it is for likelihood computation. Finally, we look at the effect of the configuration of the enrollment data on PLDA scoring, studying the properties of conditional dependence and number-of-enrollment-utterances per target speaker. Our experiments indicate that these properties affect the performance of the PLDA model. These results further support the conclusion that i-vector averaging is a simple and effective way to process multiple enrollment utterances.

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Section: Introductionmentioning

confidence: 99%

From single to multiple enrollment i-vectors: Practical PLDA scoring variants for speaker verification

Rajan

Afanasyev

Hautamäki

et al. 2014

Digital Signal Processing

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“…For most of real applications noise free data cannot be guaranteed, hence developing robust speech recognition systems which works well in presence of noise is important. Mandasari et al (2012) compared the robustness of some standard speaker recognition approaches in the presence of noise. The compared techniques were GMM based dot scoring system, i-vector based LDA ?…”

Section: Noisy Datamentioning

confidence: 99%

i-Vectors in speech processing applications: a survey

Verma

Das

2015

Int J Speech Technol

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In the domain of speech recognition many methods have been proposed over time like Gaussian mixture models (GMM), GMM with universal background model (GMM-UBM framework), joint factor analysis, etc. i-Vector subspace modeling is one of the recent methods that has become the state of the art technique in this domain. This method largely provides the benefit of modeling both the intra-domain and inter-domain variabilities into the same low dimensional space. In this survey, we present a comprehensive collection of research work related to i-vectors since its inception. Some recent trends of using i-vectors in combination with other approaches are also discussed. The application of i-vectors in various fields of speech recognition, viz speaker, language, accent recognition, etc. is also presented. This paper should serve as a good starting point for anyone interested in working with i-vectors for speech processing in general. We then conclude the paper with a brief discussion on the future of i-vectors.

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“…Next we consider large s p and s d sets of scores from a speaker recognition system based on the Probabilistic Linear Discriminant Analysis (PLDA) [44] approach which models the distribution of i-vectors as a multivariate Gaussian. The system is described in [ Table 3.…”

Section: Selection Of Pdfsmentioning

confidence: 99%