9,465 research outputs found
NPLDA: A Deep Neural PLDA Model for Speaker Verification
The state-of-art approach for speaker verification consists of a neural
network based embedding extractor along with a backend generative model such as
the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose
a neural network approach for backend modeling in speaker recognition. 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. The proposed model, termed as
neural PLDA (NPLDA), is initialized using the generative PLDA model parameters.
The loss function for the NPLDA model is an approximation of the minimum
detection cost function (DCF). The speaker recognition experiments using the
NPLDA model are performed on the speaker verificiation task in the VOiCES
datasets as well as the SITW challenge dataset. In these experiments, the NPLDA
model optimized using the proposed loss function improves significantly over
the state-of-art PLDA based speaker verification system.Comment: Published in Odyssey 2020, the Speaker and Language Recognition
Workshop (VOiCES Special Session). Link to GitHub Implementation:
https://github.com/iiscleap/NeuralPlda. arXiv admin note: substantial text
overlap with arXiv:2001.0703
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
In this paper, we propose an efficient semidefinite programming (SDP)
approach to worst-case linear discriminant analysis (WLDA). Compared with the
traditional LDA, WLDA considers the dimensionality reduction problem from the
worst-case viewpoint, which is in general more robust for classification.
However, the original problem of WLDA is non-convex and difficult to optimize.
In this paper, we reformulate the optimization problem of WLDA into a sequence
of semidefinite feasibility problems. To efficiently solve the semidefinite
feasibility problems, we design a new scalable optimization method with
quasi-Newton methods and eigen-decomposition being the core components. The
proposed method is orders of magnitude faster than standard interior-point
based SDP solvers.
Experiments on a variety of classification problems demonstrate that our
approach achieves better performance than standard LDA. Our method is also much
faster and more scalable than standard interior-point SDP solvers based WLDA.
The computational complexity for an SDP with constraints and matrices of
size by is roughly reduced from to
( in our case).Comment: 14 page
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