Text-Dependent Speaker Verification
2 papers with code • 0 benchmarks • 0 datasets
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On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification
Furthermore, we study a range of loss functions when speaker identity is used as the training target.
Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification
This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV).
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification
In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV).
Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding
In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision.
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification
A fusion of the x-vector/PLDA baseline and the SID/PLDA scores prior to PID fusion further improved performance by 15% indicating complementarity of the proposed approach to the x-vector system.
UIAI System for Short-Duration Speaker Verification Challenge 2020
Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0. 072 and an equal error rate (EER) of 2. 14% on the evaluation set.
On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors
We further investigate the impact of the different bottleneck (BN) features on the performance of x-vectors, including the recently-introduced time-contrastive-learning (TCL) BN features and phone-discriminant BN features.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021.
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database
We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR.
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).