ResearchTrend.AI
Speaker diarisation using 2D self-attentive combination of embeddings

Speaker diarisation using 2D self-attentive combination of embeddings

Guangzhi Sun
Chao Zhang
P. Woodland
ArXivPDFHTML

Papers citing "Speaker diarisation using 2D self-attentive combination of embeddings"

8 / 8 papers shown
Title
High Order Recurrent Neural Networks for Acoustic Modelling
High Order Recurrent Neural Networks for Acoustic Modelling
Chuxu Zhang
P. Woodland
ODL
63
16
0
22 Feb 2018
Attention-Based Models for Text-Dependent Speaker Verification
Attention-Based Models for Text-Dependent Speaker Verification
F. R. Chowdhury
Quan Wang
Ignacio López Moreno
Li Wan
55
172
0
28 Oct 2017
Speaker Diarization with LSTM
Speaker Diarization with LSTM
Quan Wang
Carlton Downey
Li Wan
Philip Mansfield
Ignacio López Moreno
54
316
0
28 Oct 2017
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings
Pawel Cyrta
Tomasz Trzciñski
Wojciech Stokowiec
42
33
0
09 Aug 2017
Deep Speaker Feature Learning for Text-independent Speaker Verification
Deep Speaker Feature Learning for Text-independent Speaker Verification
Lantian Li
Yixiang Chen
Ying Shi
Zhiyuan Tang
Dong Wang
54
86
0
10 May 2017
A Structured Self-attentive Sentence Embedding
A Structured Self-attentive Sentence Embedding
Zhouhan Lin
Minwei Feng
Cicero Nogueira dos Santos
Mo Yu
Bing Xiang
Bowen Zhou
Yoshua Bengio
113
2,139
0
09 Mar 2017
End-to-End Attention based Text-Dependent Speaker Verification
End-to-End Attention based Text-Dependent Speaker Verification
Shi-Xiong Zhang
Zhuo Chen
Yong Zhao
Jinyu Li
Jiawei Liu
68
177
0
03 Jan 2017
End-to-End Text-Dependent Speaker Verification
End-to-End Text-Dependent Speaker Verification
G. Heigold
Ignacio López Moreno
Samy Bengio
Noam M. Shazeer
63
586
0
27 Sep 2015

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.