5 research outputs found
Large scale training of Pairwise Support Vector Machines for speaker recognition
State–of–the–art systems for text–independent speaker recognition use as their features a compact representation of a speaker utterance, known as “i–vector”. We recently presented an efficient approach for training a Pairwise Support Vector Machine (PSVM) with a suitable kernel for i–vector pairs for a quite large speaker recognition task. Rather than estimating an SVM model per speaker, according to the “one versus all” discriminative paradigm, the PSVM approach classifies a trial, consisting of a pair of i–vectors, as belonging or not to the same speaker class. Training a PSVM with large amount of data, however, is a memory and computational
expensive task, because the number of training pairs grows
quadratically with the number of training i–vectors. This paper
demonstrates that a very small subset of the training pairs is necessary to train the original PSVM model, and proposes two approaches that allow discarding most of the training pairs that are not essential, without harming the accuracy of the model. This allows dramatically reducing the memory and computational resources needed for training, which becomes feasible with large datasets including many speakers. We have assessed these approaches on the extended core conditions of the NIST 2012 Speaker Recognition Evaluation. Our results show that the accuracy of the PSVM trained with a sufficient number of speakers is 10-30% better compared to the one obtained by a PLDA model, depending on the testing conditions. Since the PSVM accuracy increases with the training set size, but PSVM training does not scale well for large numbers of speakers, our selection techniques become relevant for training accurate discriminative classifiers
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