13 citations · 126 across the 71 of their papers we have counts for
10 papers · 1 filter
Human and Machine Speaker Recognition Based on Short Trivial Events
Miao Zhang, Xiaofei Kang, Yanqing Wang +4
Trivial events are ubiquitous in human to human conversations, e.g., cough, laugh and sniff. Compared to regular speech, these trivial events are usually short and unclear, thus ge…
Full-info Training for Deep Speaker Feature Learning
Lantian Li, Zhiyuan Tang, Dong Wang +1
In recent studies, it has shown that speaker patterns can be learned from very short speech segments (e.g., 0.3 seconds) by a carefully designed convolutional & time-delay deep neu…
Deep Factorization for Speech Signal
Dong Wang, Lantian Li, Ying Shi +2
Speech signals are complex intermingling of various informative factors, and this information blending makes decoding any of the individual factors extremely difficult. A natural i…
Cross-lingual Speaker Verification with Deep Feature Learning
Lantian Li, Dong Wang, Askar Rozi +1
Existing speaker verification (SV) systems often suffer from performance degradation if there is any language mismatch between model training, speaker enrollment, and test. A major…
Speaker Recognition with Cough, Laugh and "Wei"
Miao Zhang, Yixiang Chen, Lantian Li +1
This paper proposes a speaker recognition (SRE) task with trivial speech events, such as cough and laugh. These trivial events are ubiquitous in conversations and less subjected to…
Deep Speaker Verification: Do We Need End to End?
Dong Wang, Lantian Li, Zhiyuan Tang +1
End-to-end learning treats the entire system as a whole adaptable black box, which, if sufficient data are available, may learn a system that works very well for the target task. T…
A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification
Lantian Li, Yixiang Chen, Dong Wang +1
For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to b…
Phone-aware Neural Language Identification
Zhiyuan Tang, Dong Wang, Yixiang Chen +2
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlo…
Deep Speaker Feature Learning for Text-independent Speaker Verification
Lantian Li, Yixiang Chen, Ying Shi +2
Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, e…
Phonetic Temporal Neural Model for Language Identification
Zhiyuan Tang, Dong Wang, Yixiang Chen +2
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overloo…