145,058 research outputs found
On deep speaker embeddings for text-independent speaker recognition
We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the
last classification layer of a classification neural network instead of a
simple softmax activation allows to train a more generalized discriminative
speaker embedding extractor. Cosine similarity is an effective metric for
speaker verification in this embedding space. We also address the problem of
choosing an architecture for the extractor. We found that deep networks with
residual frame level connections outperform wide but relatively shallow
architectures. This paper also proposes several improvements for previous
DNN-based extractor systems to increase the speaker recognition accuracy. We
show that the discriminatively trained similarity metric learning approach
outperforms the standard LDA-PLDA method as an embedding backend. The results
obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate
robustness of the proposed systems when dealing with close to real-life
conditions.Comment: Submitted to Odyssey 201
Speaker characterization by means of attention pooling
State-of-the-art Deep Learning systems for speaker verification are commonly based on speaker embedding extractors. These architectures are usually composed of a feature extractor front-end together with a pooling layer to encode variable length utterances into fixed-length speaker vectors. The authors have recently proposed the use of a Double Multi-Head Self Attention pooling for speaker recognition, placed between a CNN-based front-end and a set of fully connected layers. This has shown to be an excellent approach to efficiently select the most relevant features captured by the front-end from the speech signal. In this paper we show excellent experimental results by adapting this architecture to other different speaker characterization tasks, such as emotion recognition, sex classification and COVID-19 detection.Peer ReviewedPostprint (published version
Additive Margin SincNet for Speaker Recognition
Speaker Recognition is a challenging task with essential applications such as
authentication, automation, and security. The SincNet is a new deep learning
based model which has produced promising results to tackle the mentioned task.
To train deep learning systems, the loss function is essential to the network
performance. The Softmax loss function is a widely used function in deep
learning methods, but it is not the best choice for all kind of problems. For
distance-based problems, one new Softmax based loss function called Additive
Margin Softmax (AM-Softmax) is proving to be a better choice than the
traditional Softmax. The AM-Softmax introduces a margin of separation between
the classes that forces the samples from the same class to be closer to each
other and also maximizes the distance between classes. In this paper, we
propose a new approach for speaker recognition systems called AM-SincNet, which
is based on the SincNet but uses an improved AM-Softmax layer. The proposed
method is evaluated in the TIMIT dataset and obtained an improvement of
approximately 40% in the Frame Error Rate compared to SincNet
Speaker identification and clustering using convolutional neural networks
Deep learning, especially in the form of convolutional neural networks (CNNs), has triggered substantial improvements in computer vision and related fields in recent years. This progress is attributed to the shift from designing features and subsequent individual sub-systems towards learning features and recognition systems end to end from nearly unprocessed data. For speaker clustering, however, it is still common to use handcrafted processing chains such as MFCC features and GMM-based models. In this paper, we use simple spectrograms as input to a CNN and study the optimal design of those networks for speaker identification and clustering. Furthermore, we elaborate on the question how to transfer a network, trained for speaker identification, to speaker clustering. We demonstrate our approach on the well known TIMIT dataset, achieving results comparable with the state of the art – without the need for handcrafted features
Self-supervised deep learning approaches to speaker recognition: A Ph.D. Thesis overview
Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in prac- tice. In i-vector based speaker recognition, cosine (unsuper- vised) and PLDA (supervised) are the basic scoring techniques, with a big performance gap between the two. In this thesis we tried to fill this gap without using speaker labels in several ways. We applied Restricted Boltzmann Machine (RBM) vectors for the tasks of speaker clustering and tracking in TV broadcast shows. The experiments on AGORA database show that us- ing this approach we gain a relative improvement of 12% and 11% for speaker clustering and tracking tasks, respectively. We also applied DL techniques in order to increase the discrimina- tive power of i-vectors in speaker verification task, for which we have proposed the use of autoencoder in several ways, i.e., (1) as a pre-training for a Deep Neural Network (DNN), (2) as a near- est neighbor autoencoder for i-vectors, (3) as an average pooled nearest neighbor autoencoder. The experiments on VoxCeleb database show that we gain a relative improvement of 21%, 42% and 53%, using the three systems respectively. Finally we also proposed a self-supervised end-to-end speaker verification system. The architecture is based on a Convolutional Neural Network (CNN), trained as a siamese network with multiple branches. From the results we can see that our system shows comparable performance to a supervised baselineThis work was supported by the project PID2019-107579RBI00 / AEI / 10.13039/501100011033Peer ReviewedPostprint (published version
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