111 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
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw features. This paper presents a convolutional time-delay deep
neural network structure (CT-DNN) for speaker feature learning. Our
experimental results on the Fisher database demonstrated that this CT-DNN can
produce high-quality speaker features: even with a single feature (0.3 seconds
including the context), the EER can be as low as 7.68%. This effectively
confirmed that the speaker trait is largely a deterministic short-time property
rather than a long-time distributional pattern, and therefore can be extracted
from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur
From features to speaker vectors by means of restricted Boltzmann machine adaptation
Restricted Boltzmann Machines (RBMs) have shown success in different stages of speaker recognition systems. In this paper, we propose a novel framework to produce a vector-based representation for each speaker, which will be referred to as RBM-vector. This new approach maps the speaker spectral features to a single fixed-dimensional vector carrying speaker-specific information. In this work, a global model, referred to as Universal RBM (URBM), is trained taking advantage of RBM unsupervised learning capabilities. Then, this URBM is adapted
to the data of each speaker in the development, enrolment and
evaluation datasets. The network connection weights of the adapted RBMs are further concatenated and subject to a whitening with dimension reduction stage to build the speaker vectors. The evaluation is performed on the core test condition of the NIST SRE 2006 database, and it is shown that RBM-vectors achieve 15% relative improvement in terms of EER compared to i-vectors using cosine scoring. The score fusion with i-vector attains more than 24% relative improvement. The interest of this result for score fusion yields on the fact that both vectors are produced in an unsupervised fashion and can be used instead of i-vector/PLDA approach, when no data label is available. Results obtained for RBM-vector/PLDA framework is comparable with the ones from i-vector/PLDA. Their score fusion achieves 14% relative improvement compared to i-vector/PLDA.Peer ReviewedPostprint (published version
Deep learning methods in speaker recognition: a review
This paper summarizes the applied deep learning practices in the field of
speaker recognition, both verification and identification. Speaker recognition
has been a widely used field topic of speech technology. Many research works
have been carried out and little progress has been achieved in the past 5-6
years. However, as deep learning techniques do advance in most machine learning
fields, the former state-of-the-art methods are getting replaced by them in
speaker recognition too. It seems that DL becomes the now state-of-the-art
solution for both speaker verification and identification. The standard
x-vectors, additional to i-vectors, are used as baseline in most of the novel
works. The increasing amount of gathered data opens up the territory to DL,
where they are the most effective
Optimization of data-driven filterbank for automatic speaker verification
Most of the speech processing applications use triangular filters spaced in
mel-scale for feature extraction. In this paper, we propose a new data-driven
filter design method which optimizes filter parameters from a given speech
data. First, we introduce a frame-selection based approach for developing
speech-signal-based frequency warping scale. Then, we propose a new method for
computing the filter frequency responses by using principal component analysis
(PCA). The main advantage of the proposed method over the recently introduced
deep learning based methods is that it requires very limited amount of
unlabeled speech-data. We demonstrate that the proposed filterbank has more
speaker discriminative power than commonly used mel filterbank as well as
existing data-driven filterbank. We conduct automatic speaker verification
(ASV) experiments with different corpora using various classifier back-ends. We
show that the acoustic features created with proposed filterbank are better
than existing mel-frequency cepstral coefficients (MFCCs) and
speech-signal-based frequency cepstral coefficients (SFCCs) in most cases. In
the experiments with VoxCeleb1 and popular i-vector back-end, we observe 9.75%
relative improvement in equal error rate (EER) over MFCCs. Similarly, the
relative improvement is 4.43% with recently introduced x-vector system. We
obtain further improvement using fusion of the proposed method with standard
MFCC-based approach.Comment: Published in Digital Signal Processing journal (Elsevier
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