19 citations · 32 across the 13 of their papers we have counts for
6 papers · 2 filters
Multi-task Metric Learning for Text-independent Speaker Verification
Yafeng Chen, Wu Guo, Jingjing Shi +2
In this work, we introduce metric learning (ML) to enhance the deep embedding learning for text-independent speaker verification (SV). Specifically, the deep speaker embedding netw…
Exploring Universal Speech Attributes for Speaker Verification with an Improved Cross-stitch Network
Jiajun Qi, Wu Guo, Jingjing Shi +2
The universal speech attributes for x-vector based speaker verification (SV) are addressed in this paper. The manner and place of articulation form the fundamental speech attribute…
An Adaptive X-vector Model for Text-independent Speaker Verification
Bin Gu, Wu Guo, Lirong Dai +1
In this paper, adaptive mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification. First, adaptive convolutional neural…
Gaussian speaker embedding learning for text-independent speaker verification
Bin Gu, Wu Guo
The x-vector maps segments of arbitrary duration to vectors of fixed dimension using deep neural network. Combined with the probabilistic linear discriminant analysis (PLDA) backen…
An Improved Deep Neural Network for Modeling Speaker Characteristics at Different Temporal Scales
Bin Gu, Wu Guo
This paper presents an improved deep embedding learning method based on convolutional neural network (CNN) for text-independent speaker verification. Two improvements are proposed…
Attentive batch normalization for lstm-based acoustic modeling of speech recognition
Fenglin Ding, Wu Guo, Lirong Dai +1
Batch normalization (BN) is an effective method to accelerate model training and improve the generalization performance of neural networks. In this paper, we propose an improved ba…