3,168 research outputs found
Factorization of Discriminatively Trained i-vector Extractor for Speaker Recognition
In this work, we continue in our research on i-vector extractor for speaker
verification (SV) and we optimize its architecture for fast and effective
discriminative training. We were motivated by computational and memory
requirements caused by the large number of parameters of the original
generative i-vector model. Our aim is to preserve the power of the original
generative model, and at the same time focus the model towards extraction of
speaker-related information. We show that it is possible to represent a
standard generative i-vector extractor by a model with significantly less
parameters and obtain similar performance on SV tasks. We can further refine
this compact model by discriminative training and obtain i-vectors that lead to
better performance on various SV benchmarks representing different acoustic
domains.Comment: Submitted to Interspeech 2019, Graz, Austria. arXiv admin note:
substantial text overlap with arXiv:1810.1318
Speaker verification using sequence discriminant support vector machines
This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels generalize Fisher kernels and are based on underlying generative models such as Gaussian mixture models (GMMs). This approach provides direct discrimination between whole sequences, in contrast with the frame-level approaches at the heart of most current systems. The resultant SVMs have a very high dimensionality since it is related to the number of parameters in the underlying generative model. To address problems that arise in the resultant optimization we introduce a technique called spherical normalization that preconditions the Hessian matrix. We have performed speaker verification experiments using the PolyVar database. The SVM system presented here reduces the relative error rates by 34% compared to a GMM likelihood ratio system
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
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