17 citations · 36 across the 14 of their papers we have counts for
14 papers
Graph Neural Network Backend for Speaker Recognition
Liang He, Ruida Li, Mengqi Niu
Currently, most speaker recognition backends, such as cosine, linear discriminant analysis (LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculatin…
I4U System Description for NIST SRE'20 CTS Challenge
Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…
Robust Training for Speaker Verification against Noisy Labels
Zhihua Fang, Liang He, Hanhan Ma +2
The deep learning models used for speaker verification rely heavily on large amounts of data and correct labeling. However, noisy (incorrect) labels often occur, which degrades the…
THUEE system description for NIST 2019 SRE CTS Challenge
Yi Liu, Tianyu Liang, Can Xu +9
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the…
Adaptive Multi-scale Detection of Acoustic Events
Wenhao Ding, Liang He
The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. This task plays a significant role in sa…
Latent Class Model with Application to Speaker Diarization
Liang He, Xianhong Chen, Can Xu +3
In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft informat…
Large Margin Softmax Loss for Speaker Verification
Yi Liu, Liang He, Jia Liu
In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of…
Multi-Scale Time-Frequency Attention for Acoustic Event Detection
Jingyang Zhang, Wenhao Ding, Jintao Kang +1
Most attention-based methods only concentrate along the time axis, which is insufficient for Acoustic Event Detection (AED). Meanwhile, previous methods for AED rarely considered t…
Exploring a Unified Attention-Based Pooling Framework for Speaker Verification
Yi Liu, Liang He, Weiwei Liu +1
The pooling layer is an essential component in the neural network based speaker verification. Most of the current networks in speaker verification use average pooling to derive the…
Multiobjective Optimization Training of PLDA for Speaker Verification
Liang He, Xianhong Chen, Can Xu +1
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers. The parameters of P…
Speaker Embedding Extraction with Phonetic Information
Yi Liu, Liang He, Jia Liu +1
Speaker embeddings achieve promising results on many speaker verification tasks. Phonetic information, as an important component of speech, is rarely considered in the extraction o…
MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks
Wenhao Ding, Liang He
In this paper, we propose an enhanced triplet method that improves the encoding process of embeddings by jointly utilizing generative adversarial mechanism and multitasking optimiz…
Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification
Yi Liu, Liang He, Weiqiang Zhang +2
Frame alignments can be computed by different methods in GMM-based speaker verification. By incorporating a phonetic Gaussian mixture model (PGMM), we are able to compare the perfo…
Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification
Yi Liu, Liang He, Yao Tian +3
Text-dependent speaker verification is becoming popular in the speaker recognition society. However, the conventional i-vector framework which has been successful for speaker ident…