Text-Dependent Speaker Verification
2 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV).
Adversarial Speaker Verification
The use of deep networks to extract embeddings for speaker recognition has proven successfully.
Optimization of the Area Under the ROC Curve using Neural Network Supervectors for Text-Dependent Speaker Verification
This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks.
Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification
Moreover, we can apply a convolutional neural network as front-end, and thanks to the alignment process being differentiable, we can train the whole network to produce a supervector for each utterance which will be discriminative with respect to the speaker and the phrase simultaneously.
Spoken Pass-Phrase Verification in the i-vector Space
The task of spoken pass-phrase verification is to decide whether a test utterance contains the same phrase as given enrollment utterances.
Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification
Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.
On Residual CNN in text-dependent speaker verification task
Deep learning approaches are still not very common in the speaker verification field.
Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification
It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval, and the TCL method aims to exploit this temporal structure.
End-to-End Attention based Text-Dependent Speaker Verification
A new type of End-to-End system for text-dependent speaker verification is presented in this paper.
Incorporating Pass-Phrase Dependent Background Models for Text-Dependent Speaker Verification
In this paper, we propose pass-phrase dependent background models (PBMs) for text-dependent (TD) speaker verification (SV) to integrate the pass-phrase identification process into the conventional TD-SV system, where a PBM is derived from a text-independent background model through adaptation using the utterances of a particular pass-phrase.
