352 research outputs found
Speaker recognition by means of restricted Boltzmann machine adaptation
Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft
A Generative Model for Score Normalization in Speaker Recognition
We propose a theoretical framework for thinking about score normalization,
which confirms that normalization is not needed under (admittedly fragile)
ideal conditions. If, however, these conditions are not met, e.g. under
data-set shift between training and runtime, our theory reveals dependencies
between scores that could be exploited by strategies such as score
normalization. Indeed, it has been demonstrated over and over experimentally,
that various ad-hoc score normalization recipes do work. We present a first
attempt at using probability theory to design a generative score-space
normalization model which gives similar improvements to ZT-norm on the
text-dependent RSR 2015 database
Deep learning backend for single and multisession i-vector speaker recognition
The lack of labeled background data makes a big performance gap between cosine and Probabilistic Linear Discriminant Analysis (PLDA) scoring baseline techniques for i-vectors in speaker recognition. Although there are some unsupervised clustering techniques to estimate the labels, they cannot accurately predict the true labels and they also assume that there are several samples from the same speaker in the background data that could not be true in reality. In this paper, the authors make use of Deep Learning (DL) to fill this performance gap given unlabeled background data. To this goal, the authors have proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on deep belief networks and deep neural networks to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single- and multisession speaker enrollment tasks, some experiments have been carried out in this paper in both scenarios. Experiments on National Institute of Standards and Technology 2014 i-vector challenge show that 46% of this performance gap, in terms of minimum of the decision cost function, is filled by the proposed DL-based system. Furthermore, the score combination of the proposed DL-based system and PLDA with estimated labels covers 79% of this gap.Peer ReviewedPostprint (published version
A Speaker Verification Backend with Robust Performance across Conditions
In this paper, we address the problem of speaker verification in conditions
unseen or unknown during development. A standard method for speaker
verification consists of extracting speaker embeddings with a deep neural
network and processing them through a backend composed of probabilistic linear
discriminant analysis (PLDA) and global logistic regression score calibration.
This method is known to result in systems that work poorly on conditions
different from those used to train the calibration model. We propose to modify
the standard backend, introducing an adaptive calibrator that uses duration and
other automatically extracted side-information to adapt to the conditions of
the inputs. The backend is trained discriminatively to optimize binary
cross-entropy. When trained on a number of diverse datasets that are labeled
only with respect to speaker, the proposed backend consistently and, in some
cases, dramatically improves calibration, compared to the standard PLDA
approach, on a number of held-out datasets, some of which are markedly
different from the training data. Discrimination performance is also
consistently improved. We show that joint training of the PLDA and the adaptive
calibrator is essential -- the same benefits cannot be achieved when freezing
PLDA and fine-tuning the calibrator. To our knowledge, the results in this
paper are the first evidence in the literature that it is possible to develop a
speaker verification system with robust out-of-the-box performance on a large
variety of conditions
Neural PLDA Modeling for End-to-End Speaker Verification
While deep learning models have made significant advances in supervised
classification problems, the application of these models for out-of-set
verification tasks like speaker recognition has been limited to deriving
feature embeddings. The state-of-the-art x-vector PLDA based speaker
verification systems use a generative model based on probabilistic linear
discriminant analysis (PLDA) for computing the verification score. Recently, we
had proposed a neural network approach for backend modeling in speaker
verification called the neural PLDA (NPLDA) where the likelihood ratio score of
the generative PLDA model is posed as a discriminative similarity function and
the learnable parameters of the score function are optimized using a
verification cost. In this paper, we extend this work to achieve joint
optimization of the embedding neural network (x-vector network) with the NPLDA
network in an end-to-end (E2E) fashion. This proposed end-to-end model is
optimized directly from the acoustic features with a verification cost function
and during testing, the model directly outputs the likelihood ratio score. With
various experiments using the NIST speaker recognition evaluation (SRE) 2018
and 2019 datasets, we show that the proposed E2E model improves significantly
over the x-vector PLDA baseline speaker verification system.Comment: Accepted in Interspeech 2020. GitHub Implementation Repos:
https://github.com/iiscleap/E2E-NPLDA and
https://github.com/iiscleap/NeuralPld
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