5 research outputs found
Gaussian-Constrained training for speaker verification
Neural models, in particular the d-vector and x-vector architectures, have
produced state-of-the-art performance on many speaker verification tasks.
However, two potential problems of these neural models deserve more
investigation. Firstly, both models suffer from `information leak', which means
that some parameters participating in model training will be discarded during
inference, i.e, the layers that are used as the classifier. Secondly, these
models do not regulate the distribution of the derived speaker vectors. This
`unconstrained distribution' may degrade the performance of the subsequent
scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
training approach that (1) discards the parametric classifier, and (2) enforces
the distribution of the derived speaker vectors to be Gaussian. Our experiments
on the VoxCeleb and SITW databases demonstrated that this new training approach
produced more representative and regular speaker embeddings, leading to
consistent performance improvement
Gaussian speaker embedding learning for text-independent speaker verification
The x-vector maps segments of arbitrary duration to vectors of fixed
dimension using deep neural network. Combined with the probabilistic linear
discriminant analysis (PLDA) backend, the x-vector/PLDA has become the dominant
framework in text-independent speaker verification. Nevertheless, how to
extract the x-vector appropriate for the PLDA backend is a key problem. In this
paper, we propose a Gaussian noise constrained network (GNCN) to extract
xvector, which adopts a multi-task learning strategy with the primary task
classifying the speakers and the auxiliary task just fitting the Gaussian
noises. Experiments are carried out using the SITW database. The results
demonstrate the effectiveness of our proposed methodComment: 5 pages, 3 figure
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments
The explosion of available speech data and new speaker modeling methods based
on deep neural networks (DNN) have given the ability to develop more robust
speaker recognition systems. Among DNN speaker modelling techniques, x-vector
system has shown a degree of robustness in noisy environments. Previous studies
suggest that by increasing the number of speakers in the training data and
using data augmentation more robust speaker recognition systems are achievable
in noisy environments. In this work, we want to know if explicit noise
compensation techniques continue to be effective despite the general noise
robustness of these systems. For this study, we will use two different x-vector
networks: the first one is trained on Voxceleb1 (Protocol1), and the second one
is trained on Voxceleb1+Voxveleb2 (Protocol2). We propose to add a denoising
x-vector subsystem before scoring. Experimental results show that, the x-vector
system used in Protocol2 is more robust than the other one used Protocol1.
Despite this observation we will show that explicit noise compensation gives
almost the same EER relative gain in both protocols. For example, in the
Protocol2 we have 21% to 66% improvement of EER with denoising techniques
Deep Normalization for Speaker Vectors
Deep speaker embedding has demonstrated state-of-the-art performance in
speaker recognition tasks. However, one potential issue with this approach is
that the speaker vectors derived from deep embedding models tend to be
non-Gaussian for each individual speaker, and non-homogeneous for distributions
of different speakers. These irregular distributions can seriously impact
speaker recognition performance, especially with the popular PLDA scoring
method, which assumes homogeneous Gaussian distribution. In this paper, we
argue that deep speaker vectors require deep normalization, and propose a deep
normalization approach based on a novel discriminative normalization flow (DNF)
model. We demonstrate the effectiveness of the proposed approach with
experiments using the widely used SITW and CNCeleb corpora. In these
experiments, the DNF-based normalization delivered substantial performance
gains and also showed strong generalization capability in out-of-domain tests
Speaker Recognition Based on Deep Learning: An Overview
Speaker recognition is a task of identifying persons from their voices.
Recently, deep learning has dramatically revolutionized speaker recognition.
However, there is lack of comprehensive reviews on the exciting progress.
In this paper, we review several major subtasks of speaker recognition,
including speaker verification, identification, diarization, and robust speaker
recognition, with a focus on deep-learning-based methods. Because the major
advantage of deep learning over conventional methods is its representation
ability, which is able to produce highly abstract embedding features from
utterances, we first pay close attention to deep-learning-based speaker feature
extraction, including the inputs, network structures, temporal pooling
strategies, and objective functions respectively, which are the fundamental
components of many speaker recognition subtasks. Then, we make an overview of
speaker diarization, with an emphasis of recent supervised, end-to-end, and
online diarization. Finally, we survey robust speaker recognition from the
perspectives of domain adaptation and speech enhancement, which are two major
approaches of dealing with domain mismatch and noise problems. Popular and
recently released corpora are listed at the end of the paper