7,348 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
Attentive Statistics Pooling for Deep Speaker Embedding
This paper proposes attentive statistics pooling for deep speaker embedding
in text-independent speaker verification. In conventional speaker embedding,
frame-level features are averaged over all the frames of a single utterance to
form an utterance-level feature. Our method utilizes an attention mechanism to
give different weights to different frames and generates not only weighted
means but also weighted standard deviations. In this way, it can capture
long-term variations in speaker characteristics more effectively. An evaluation
on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal
error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively.Comment: Proc. Interspeech 2018, pp2252--2256. arXiv admin note: text overlap
with arXiv:1809.0931
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