PLDA Modeling in the Fishervoice Subspace for Speaker Verification (original) (raw)
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PLDA in the i-supervector space for text-independent speaker verification
In this paper, we advocate the use of the uncompressed form of i-vector and depend on subspace modeling using probabilistic linear discriminant analysis (PLDA) in handling the speaker and session (or channel) variability. An i-vector is a low-dimensional vector containing both speaker and channel information acquired from a speech segment. When PLDA is used on an i-vector, dimension reduction is performed twice: first in the i-vector extraction process and second in the PLDA model. Keeping the full dimensionality of the i-vector in the i-supervector space for PLDA modeling and scoring would avoid unnecessary loss of information. We refer to the uncompressed i-vector as the i-supervector. The drawback in using the i-supervector with PLDA is the inversion of large matrices in the estimation of the full posterior distribution, which we show can be solved rather efficiently by portioning large matrices into smaller blocks. We also introduce the Gaussianized rank-norm, as an alternative to whitening, for feature normalization prior to PLDA modeling. We found that the i-supervector performs better during normalization. A better performance is obtained by combining the i-supervector and i-vector at the score level. Furthermore, we also analyze the computational complexity of the i-supervector system, compared with that of the i-vector, at four different stages of loading matrix estimation, posterior extraction, PLDA modeling, and PLDA scoring.
PLDA based Speaker Verification with Weighted LDA Techniques
The Speaker and Language Recognition Workshop (Odyssey 2012), 2012
This paper investigates the use of the dimensionality-reduction techniques weighted linear discriminant analysis (WLDA), and weighted median fisher discriminant analysis (WMFD), before probabilistic linear discriminant analysis (PLDA) modeling for the purpose of improving speaker verification performance in the presence of high inter-session variability. Recently it was shown that WLDA techniques can provide improvement over traditional linear discriminant analysis (LDA) for channel compensation in i-vector based speaker verification systems. We show in this paper that the speaker discriminative information that is available in the distance between pair of speakers clustered in the development i-vector space can also be exploited in heavy-tailed PLDA modeling by using the weighted dis-criminant approaches prior to PLDA modeling. Based upon the results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset, we believe that WLDA and WMFD projections before PLDA modeling can provide an improved approach when compared to uncompensated PLDA modeling for i-vector based speaker verification systems.
CHANNEL ADAPTATION OF PLDA FOR TEXT-INDEPENDENT SPEAKER VERIFICATION
Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling channel variability in the i-vector space for text-independent speaker verification. Speaker verification is a binary hypothesis testing. Given a test segment, the verification score could be computed as the log-likelihood ratio between a speaker-adapted PLDA and the universal PLDA model. This work proposes to infer the channel factor specific to each test segment and to include the channel estimate in the PLDA models, which essentially shifts the scoring function to better match that of the test channel. We also explore the influence of covariance adaptation in both speaker and channel adaptations. Experimental results on NIST SRE'08 and SRE'10 dataset confirm that the proposed channel adaptation can be effective when the covariance is kept un-adapted, while the covariance adaptation is necessary in the speaker adaptation.
Discriminatively trained Probabilistic Linear Discriminant Analysis for speaker verification
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Recently, i-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) have proven to provide state-of-the-art speaker verification performance. In this paper, the speaker verification score for a pair of i-vectors representing a trial is computed with a functional form derived from the successful PLDA generative model. In our case, however, parameters of this function are estimated based on a discriminative training criterion. We propose to use the objective function to directly address the task in speaker verification: discrimination between same-speaker and different-speaker trials. Compared with a baseline which uses a generatively trained PLDA model, discriminative training provides up to 40% relative improvement on the NIST SRE 2010 evaluation task.
Speaker-Aware Linear Discriminant Analysis in Speaker Verification
Interspeech 2020
Linear discriminant analysis (LDA) is an effective and widely used discriminative technique for speaker verification. However, it only utilizes the information on global structure to perform classification. Some variants of LDA, such as local pairwise LDA (LPLDA), are proposed to preserve more information on the local structure in the linear projection matrix. However, considering that the local structure may vary a lot in different regions, summing up related components to construct a single projection matrix may not be sufficient. In this paper, we present a speaker-aware strategy focusing on preserving distinct information on local structure in a set of linear discriminant projection matrices, and allocating them to different local regions for dimension reduction and classification. Experiments on NIST SRE2010 and NIST SRE2016 show that the speaker-aware strategy can boost the performance of both LDA and LPLDA backends in i-vector systems and x-vector systems.
Exploring kernel discriminant analysis for speaker verification with limited test data
Pattern Recognition Letters, 2017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • A novel framework for channel/session compensation in i-vector speaker modeling. • Explore non-linearity in channel/session information at i-vector framework. • Effectiveness of kernel discriminant analysis (KDA) with higher dimension. • Significance of KDA for speaker verification with limited test data.
Alleviating the small sample-size problem in i-vector based speaker verification
2012 8th International Symposium on Chinese Spoken Language Processing, 2012
This paper investigates the small sample-size problem in i-vector based speaker verification systems. The idea of i-vectors is to represent the characteristics of speakers in the factors of a factor analyzer. Because the factor loading matrix defines the possible speakerand channel-variability of i-vectors, it is important to suppress the unwanted channel variability. Linear discriminant analysis (LDA), within-class covariance normalization (WCCN), and probabilistic LDA are commonly used for such purpose. These methods, however, require training data comprising many speakers each providing sufficient recording sessions for good performance. Performance will suffer when the number of speakers and/or number of sessions per speaker are too small. This paper compares four approaches to addressing this small sample-size problem: (1) preprocessing the ivectors by PCA before applying LDA (PCA+LDA), (2) replacing the matrix inverse in LDA by pseudo-inverse, (3) applying multiway LDA by exploiting the microphone and speaker labels of the training data, and (4) increasing the matrix rank in LDA by generating more i-vectors using utterance partitioning. Results based on NIST 2010 SRE suggests that utterance partitioning performs the best, followed by multi-way LDA and PCA+LDA.
Digital Signal Processing, 2014
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.
An Integration of Random Subspace Sampling and Fishervoice for Speaker Verification
2014
In this paper, we propose an integration of random subspace sampling and Fishervoice for speaker verification. In the previous random sampling framework [1], we randomly sample the JFA feature space into a set of low-dimensional subspaces. For every random subspace, we use Fishervoice to model the intrinsic vocal characteristics in a discriminant subspace. The complex speaker characteristics are modeled through multiple subspaces. Through a fusion rule, we form a more powerful and stable classifier that can preserve most of the discriminative information. But in many cases, random subspace sampling may discard too much useful discriminative information for high-dimensional feature space. Instead of increasing the number of random subspace or using more complex fusion rules which increase system complexity, we attempt to increase the performance of each individual weak classifier. Hence, we propose to investigate the integration of random subspace sampling with the Fishervoice approa...
Discriminative subspace modeling of SNR and duration variabilities for robust speaker verification
Computer Speech & Language, 2017
Although i-vectors together with probabilistic LDA (PLDA) have achieved a great success in speaker verification, how to suppress the undesirable effects caused by the variability in utterance length and background noise level is still a challenge. This paper aims to improve the robustness of i-vector based speaker verification systems by compensating for the utterance-length variability and noise-level variability. Inspired by the recent findings that noiselevel variability can be modeled by a signal-to-noise ratio (SNR) subspace and that duration variability can be modeled as additive noise in the i-vector space, we propose to add an SNR factor and a duration factor to the PLDA model. In this framework, we assume that i-vectors derived from utterances with comparable durations share similar duration-specific information and that i-vectors extracted from utterances within a narrow SNR range have similar SNR-specific information. Based on these assumptions, an i-vector can be represented as a linear combination of four components: speaker, SNR, duration, and channel. A variational Bayes algorithm is developed to infer this latent variable model via a discriminative subspace training procedure. In the testing stage, different variabilities are compensated when computing the likelihood ratio. Experiments on Common Conditions 1 and 4 in NIST 2012 SRE show that the proposed model outperforms the conventional PLDA and SNR-invariant PLDA. Results also show that the proposed model performs better than the uncertainty-propagation PLDA (UP-PLDA) for long test utterances.