262,335 research outputs found
Memory-aware i-vector extraction by means of subspace factorization
No full textMost of the state-of-the-art speaker recognition systems use i- vectors, a compact representation of spoken utterances. Since the "standard" i-vector extraction procedure requires large memory structures, we recently presented the Factorized Sub-space Estimation (FSE) approach, an efficient technique that dramatically reduces the memory needs for i-vector extraction, and is also fast and accurate compared to other proposed approaches. FSE is based on the approximation of the matrix T, representing the speaker variability sub-space, by means of the product of appropriately designed matrices. In this work, we introduce and evaluate a further approximation of the matrices that most contribute to the memory costs in the FSE approach, showing that it is possible to obtain comparable system accuracy using less than a half of FSE memory, which corresponds to more than 60 times memory reduction with respect to the standard method of i-vector extractio
E--vectors: JFA and i--vectors revisited
No full textSystems based on i-vectors represent the current state-of-the-art in text-independent speaker recognition. In this work we introduce a new compact representation of a speech segment, similar to the speaker factors of Joint Factor Analysis (JFA) and to i-vectors, that we call "e-vector". The e-vectors derive their name from the eigenvoice space of the JFA speaker modeling approach. Our working hypothesis is that JFA estimates a more informative speaker subspace than the "total variability" i-vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose, thus, a simple "i-vector style" modeling and training technique that exploits this observation, and estimates a more accurate subspace with respect to the one provided by the classical i-vector approach, as confirmed by the results of a set of tests performed on the extended core NIST 2012 Speaker Recognition Evaluation dataset. Simply replacing the i-vectors with e-vectors we get approximately 10% average improvement of the Cprimary cost function, using different systems and classifiers. These performance gains come without any additional memory or computational costs with respect to the standard i-vector systems
Non-linear i-vector transformations for PLDA based speaker recognition
No full textThis paper proposes to estimate parametric nonlinear transformations of i-vectors for speaker recognition systems based on Probabilistic Linear Discriminant Analysis (PLDA) classification. The Gaussian PLDA model assumes that the i-vectors are distributed according to the standard normal distribution. However it has been shown that the i-vectors are better modeled, for example, by Heavy-Tailed distributions, and that significant improvement of the classification performance can be obtained by whitening and length normalizing the i-vectors. In this work we propose to transform the i-vectors so that their distribution becomes more suitable to discriminate speakers using the PLDA model. This is performed by means of a sequence of affine and non-linear transformations whose parameters are obtained by Maximum Likelihood (ML) estimation on the development set. Another contribution of this work is the reduction of the mismatch between the development and evaluation i-vector length distributions by means of a scaling factor tuned for the estimated i-vector distribution, rather than by means of a blind length normalization. Relative improvement between 7% and 14% of the Detection Cost Function was obtained with the proposed technique on the NIST SRE-2010 and SRE-2012 evaluation datasets, using both the traditional GMM/UBM and the hybrid DNN/GMM based systems
I-vector transformation and scaling for PLDA based speaker recognition
No full textThis paper proposes a density model transformation for speaker recognition systems based on i-vectors and Probabilistic Linear Discriminant Analysis (PLDA) classification. The PLDA model assumes that the i-vectors are distributed according to the standard normal distribution, whereas it is well known that this is not the case. Experiments have shown that the i-vector are better modeled, for example, by a Heavy-Tailed distribution, and that significant improvement of the classification performance can be obtained by whitening and length normalizing the i-vectors. In this work we propose to transform the i-vectors, extracted ignoring the classifier that will be used, so that their distribution becomes more suitable to discriminate speakers using PLDA. This is performed by means of a sequence of affine and non-linear transformations whose parameters are obtained by Maximum Likelihood (ML) estimation on the training set. The second contribution of this work is the reduction of the mismatch between the development and test i-vector distributions by means of a scaling factor tuned for the estimated i-vector distribution, rather than by means of a blind length normalization. Our tests performed on the NIST SRE-2010 and SRE-2012 evaluation sets show that improvement of their Cost Functions of the order of 10% can be obtained for both evaluation dat
Memory and computation trade-offs for efficient i-vector extraction
No full textThis work aims at reducing the memory demand of the data structures that are usually pre-computed and stored for fast computation of the i-vectors, a compact representation of spoken utterances that is used by most state-of-the-art speaker recognition systems. We propose two new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices, and with the recently proposed fast eigen-decomposition technique. The first approach computes an i-vector in a Variational Bayes (VB) framework by iterating the estimation of one sub-block of i-vector elements at a time, keeping fixed all the others, and can obtain i-vectors as accurate as the ones obtained by the standard technique but requiring only 25% of its memory. The second technique is based on the Conjugate Gradient solution of a linear system, which is accurate and uses even less memory, but is slower than the VB approach. We analyze and compare the time and memory resources required by all these solutions, which are suited to different applications, and we show that it is possible to get accurate results greatly reducing memory demand compared with the standard solution at almost the same speed
Method and apparatus for efficient i-vector extraction
No full textMost speaker recognition systems use i-vectors which are compact representations of speaker voice characteristics. Typical i-vector extraction procedures are complex in terms of computations and memory usage. According to an embodiment, a method and corresponding apparatus for speaker identification, comprise determining a representation for each component of a variability operator, representing statistical inter- and intra-speaker variability of voice features with respect to a background statistical model, in terms of a linear operator common to all components of the variability operator and having a first dimension larger than a second dimension of the components of the variability operator; computing statistical voice characteristics of a particular speaker using the determined representations; and employing the statistical voice characteristics of the particular speaker in performing speaker recognition. Computing the voice characteristics, by using the determined representations, results in significant reduction in memory usage and possible increase in execution spee
Fast and Memory Effective I-Vector Extraction Using a Factorized Sub-Space
No full textMost of the state-of-the-art speaker recognition systems use a compact representation of spoken utterances referred to as i-vectors. Since the "standard" i-vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the storage needs for i-vector extraction, but is also fast. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than any other known method
Joint estimation of PLDA and nonlinear transformations of speaker vectors
No full textThe Gaussian probabilistic linear discriminant anal-ysis (PLDA) model assumes Gaussian distributed priors for the latent variables that represent the speaker and channel factors. Assuming that each training i-vector belongs to a different speaker, as is usually done in i-vector extraction, i-vectors generated by a PLDA model can be considered independent and identically distributed with Gaussian distribution. Thus, we have recently proposed to transform the development i-vectors so that their distribution becomes more Gaussian-like. This is obtained by means of a sequence of affine and nonlinear transformations whose parameters are trained by maximum likelihood (ML) estimation on the development set. The evaluation i-vectors are then subject to the same transformation. Although the i-vector “gaussianization” has shown to be effective, since the i-vectors extracted from segments of the same speaker are not independent, the original assumption is not satisfactory. In this work, we show that the model can be improved by properly exploiting the information about the speaker labels, which was ignored in the previous model. In particular, a more effective PLDA model can be obtained by jointly estimating the PLDA parameters and the parameters of the nonlinear transformation of the i-vectors. In other words, while the goal of the previous approach was to “gaussianize” the training i-vectors distribution, the objective of this work is to embed the estimation of the nonlinear i-vector transformation in the PLDA model estimation. We will thus refer to this model as the nonlinear PLDA model. We show that this new approach provides significant gain with respect to PLDA, and a small, yet consistent, improvement with respect to our former i-vector “gaussianization” approach, without further additional costs
PROBABILISTIC LINEAR DISCRIMINANT ANALYSIS OF I-VECTOR POSTERIOR DISTRIBUTIONS
No full textThe i-vector extraction process is affected by several factors such as the noise level, the acoustic content of the observed features, and the duration of the analyzed speech segment. These factors influence both the i-vector estimate and its uncertainty, represented by the i- vector posterior covariance. This paper present a new PLDA model that, unlike the standard one, exploits the intrinsic i-vector uncertainty. Since short segments are known to decrease recognition accuracy, and segment duration is the main factor affecting the i-vector covariance, we designed a set of experiments aiming at comparing the standard and the new PLDA models on short speech cuts of variable duration, randomly extracted from the conversations included in the NIST SRE 2010 female telephone extended core condition. Our results show that the new model outperforms the standard PLDA when tested on short segments, and keeps the accuracy of the latter for long enough utterances. In particular, the relative improvement is up to 13% for the EER, 5% for DCF08, and 2.5% for DCF10
Speaker Recognition Using e–Vectors
No full textSystems based on i–vectors represent the current state–of–the–art in text-independent speaker recognition. Unlike joint factor analysis (JFA), which models both speaker and intersession subspaces separately, in the i–vector approach all the important variability is modeled in a single low-dimensional subspace. This paper is based on the observation that JFA estimates a more informative speaker subspace than the “total variability” i–vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose a speaker modeling approach that extracts a compact representation of a speech segment, similar to the speaker factors of JFA and to i–vectors, referred to as “e–vector.” Estimating the e–vector subspace follows a procedure similar to i–vector training, but produces a more accurate speaker subspace, as confirmed by the results of a set of tests performed on the NIST 2012 and 2010 Speaker Recognition Evaluations. Simply replacing the i–vectors with e–vectors we get approximately 10% average improvement of the C_primary cost function, using different systems and classifiers. It is worth noting that these performance gains come without any additional memory or computational costs with respect to the standard i–vector systems
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