X-Vectors: Robust DNN Embeddings for Speaker Recognition

@article{Snyder2018XVectorsRD,
  title={X-Vectors: Robust DNN Embeddings for Speaker Recognition},
  author={David Snyder and Daniel Garcia-Romero and Gregory Sell and Daniel Povey and Sanjeev Khudanpur},
  journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2018},
  pages={5329-5333}
}
In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. [] Key Method We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector…
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References

SHOWING 1-10 OF 28 REFERENCES
Deep Neural Network Embeddings for Text-Independent Speaker Verification
TLDR
It is found that the embeddings outperform i-vectors for short speech segments and are competitive on long duration test conditions, which are the best results reported for speaker-discriminative neural networks when trained and tested on publicly available corpora.
Time delay deep neural network-based universal background models for speaker recognition
TLDR
This study investigates a lightweight alternative in which a supervised GMM is derived from the TDNN posteriors, which maintains the speed of the traditional unsupervised-GMM, but achieves a 20% relative improvement in EER.
Deep Neural Networks for extracting Baum-Welch statistics for Speaker Recognition
TLDR
Although the proposed i-vectors yield inferior performance compared to the standard ones, they are capable of attaining 16% relative improvement when fused with them, meaning that they carry useful complementary information about the speaker’s identity.
Deep neural network-based speaker embeddings for end-to-end speaker verification
TLDR
It is shown that given a large number of training speakers, the proposed system outperforms an i-vector baseline in equal error-rate (EER) and at low miss rates.
Improving speaker recognition performance in the domain adaptation challenge using deep neural networks
TLDR
This paper explores the use of DNNs to collect SS for the unsupervised domain adaptation task of the Domain Adaptation Challenge (DAC), and shows that collecting SS with a DNN trained on out-of-domain data boosts the speaker recognition performance of an out- of-domain system by more than 25%.
Learning Speaker-Specific Characteristics With a Deep Neural Architecture
  • A. Salman
  • Computer Science
    IEEE Transactions on Neural Networks
  • 2011
TLDR
A novel deep neural architecture especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker- specific overcomplete representation.
Advances in deep neural network approaches to speaker recognition
TLDR
This work considers two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses a DNN during feature modeling, and several methods of DNN feature processing are applied to bring significantly greater robustness to microphone speech.
End-to-End Text-Independent Speaker Verification with Triplet Loss on Short Utterances
TLDR
An end-to-end system which directly learns a mapping from speech features to a compact fixed length speaker discriminative embedding where the Euclidean distance is employed for measuring similarity within trials.
A novel scheme for speaker recognition using a phonetically-aware deep neural network
We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for…
Front-End Factor Analysis For Speaker Verification
  • Florin Curelaru
  • Computer Science
    2018 International Conference on Communications (COMM)
  • 2018
TLDR
This paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification system and presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices.
...
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