News-2018-01-30 - 版本历史

2018年1月29日 (一) 23:14 Cslt

2018-01-29T23:14:53Z

2018年1月29日 (一) 23:14 Cslt

2018-01-29T23:14:01Z

Cslt：以“'''Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, "DEEP FACTORIZATION FOR SPEECH SIGNAL"''' This paper describes how speech signals can be factoriz...”为内容创建页面

2018-01-29T23:13:38Z

以“'''<b>Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, "DEEP FACTORIZATION FOR SPEECH SIGNAL"''' This paper describes how speech signals can be factoriz...”为内容创建页面

新页面
'''<b>Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, "DEEP FACTORIZATION FOR SPEECH SIGNAL"'''

This paper describes how speech signals can be factorized into varios informative factors in the latent
space.

More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Deep_Speech_Factorization project page]

'''Lantian Li, Zhiyuan Tang, Dong Wang, "FULL-INFO TRAINING FOR DEEP SPEAKER FEATURE LEARNING"'''

It presents a new idea to learn 'pure features', by pushing all the discriminative information to
feature learning. It provides stonger features than regular feature + softmax regression architecture.

More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Deep_Speaker_Feature_Learning project page]

'''Miao Zhang, Xiaofei Kang, Yanqing Wang, Lantian Li, Zhiyuan Tang, Haisheng Dai, Dong Wang, "HUMAN AND MACHINE SPEAKER RECOGNITION BASED ON SHORT TRIVIAL EVENTS"'''

It presents how machines can discriminate speakers by trivial events such as laugh, cough, en. <br>

More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Speaker_Recognition_on_Trivial_events project page].

@@ 第1行： / 第1行： @@
-'''Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, "DEEP FACTORIZATION FOR SPEECH SIGNAL"'''
+We have three papers aceepted by ICASSP 2018. Congratulations to the authors!
 This paper describes how speech signals can be factorized into varios informative factors in the latent
@@ 第6行： / 第8行： @@
 More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Deep_Speech_Factorization project page]
-'''Lantian Li, Zhiyuan Tang, Dong Wang, "FULL-INFO TRAINING FOR DEEP SPEAKER FEATURE LEARNING"'''
+. '''Lantian Li, Zhiyuan Tang, Dong Wang, "FULL-INFO TRAINING FOR DEEP SPEAKER FEATURE LEARNING"'''
 It presents a new idea to learn 'pure features', by pushing all the discriminative information to
@@ 第13行： / 第15行： @@
 More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Deep_Speaker_Feature_Learning project page]
+. '''Miao Zhang, Xiaofei Kang, Yanqing Wang, Lantian Li, Zhiyuan Tang, Haisheng Dai, Dong Wang, "HUMAN AND MACHINE SPEAKER RECOGNITION BASED ON SHORT TRIVIAL EVENTS"'''
 '''Miao Zhang, Xiaofei Kang, Yanqing Wang, Lantian Li, Zhiyuan Tang, Haisheng Dai, Dong Wang, "HUMAN AND MACHINE SPEAKER RECOGNITION BASED ON SHORT TRIVIAL EVENTS"'''
 It presents how machines can discriminate speakers by trivial events such as laugh, cough, en.
 More information about this research can be found in the [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/Speaker_Recognition_on_Trivial_events project page].