Full-covariance UBM and heavy-tailed PLDA in i-vector 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.
Weighted LDA Techniques for I-vector based Speaker Verification
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012
This paper introduces the Weighted Linear Discriminant Analysis (WLDA) technique, based upon the weighted pairwise Fisher criterion, for the purposes of improving i-vector speaker verification in the presence of high intersession variability. By taking advantage of the speaker discriminative information that is available in the distances between pairs of speakers clustered in the development i-vector space, the WLDA technique is shown to provide an improvement in speaker verification performance over traditional Linear Discriminant Analysis (LDA) approaches. A similar approach is also taken to extend the recently developed Source Normalised LDA (SNLDA) into Weighted SNLDA (WSNLDA) which, similarly, shows an improvement in speaker verification performance in both matched and mismatched enrolment/verification conditions. Based upon the results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset, we believe that both WLDA and WSNLDA are viable as replacement techniques to improve the performance of LDA and SNLDA-based i-vector speaker verification.
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.
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.
Source-normalised-and-weighted LDA for robust speaker recognition using i-vectors
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
The recently developed i-vector framework for speaker recognition has set a new performance standard in the research field. An i-vector is a compact representation of a speaker utterance extracted from a low-dimensional total variability subspace. Prior to classification using a cosine kernel, i-vectors are projected into an LDA space in order to reduce inter-session variability and enhance speaker discrimination. The accurate estimation of this LDA space from a training dataset is crucial to classification performance. A typical training dataset, however, does not consist of utterances acquired from all sources of interest (ie., telephone, microphone and interview speech sources) for each speaker. This has the effect of introducing source-related variation in the between-speaker covariance matrix and results in an incomplete representation of the within-speaker scatter matrix used for 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.
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.
Speaker verification using I-vector features
Thesis, 2014
Speaker recognition is a non-invasive and convenient technology that has the potential to be applied to several applications, including access control, transaction authentication over a telephone connection and forensic suspect identification by voice. Compared to many other biometrics, speaker recognition is a non-obtrusive technology and does not require special purpose acquisition hardware other than a microphone.
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.
Pattern Recognition Letters, 2013
We present a comparison of speaker verification systems based on unsupervised and supervised mixtures of probabilistic linear discriminant analysis (PLDA) models. This paper explores current applicability of unsupervised mixtures of PLDA models with Gaussian priors in a total variability space for speaker verification. Moreover, we analyze the experimental conditions under which this application is advantageous, taking into account the existing limitations of training database sizes, provided by the National Institute of Standards and Technology (NIST). We also present a full derivation of the Maximum Likelihood learning procedure for PLDA mixture.