255 citations · 780 across the 58 of their papers we have counts for
5 papers · 2 filters
Joint Separation and Denoising of Noisy Multi-talker Speech using Recurrent Neural Networks and Permutation Invariant Training
Morten Kolbæk, Dong Yu, Zheng-Hua Tan +1
In this paper we propose to use utterance-level Permutation Invariant Training (uPIT) for speaker independent multi-talker speech separation and denoising, simultaneously. Specific…
Adversarial Network Bottleneck Features for Noise Robust Speaker Verification
Hong Yu, Zheng-Hua Tan, Zhanyu Ma +1
In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an enc…
Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification
Achintya Kr. Sarkar, Zheng-Hua Tan
In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN)feature extraction method for speech signals with an application to text-dependent (TD) speaker ver…
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
Morten Kolbæk, Dong Yu, Zheng-Hua Tan +1
In this paper we propose the utterance-level Permutation Invariant Training (uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning based solution for speaker…
DNN Filter Bank Cepstral Coefficients for Spoofing Detection
Hong Yu, Zheng-Hua Tan, Zhanyu Ma +1
With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of sp…