67 citations · 380 across the 51 of their papers we have counts for
20 papers · 1 filter
What Does the Speaker Embedding Encode?
Shuai Wang, Yanmin Qian, Kai Yu
Developing a good speaker embedding has received tremendous interest in the speech community, with representations such as i-vector and d-vector demonstrating remarkable performanc…
Text adaptation for speaker verification with speaker-text factorized embeddings
Yexin Yang, Shuai Wang, Xun Gong +2
Text mismatch between pre-collected data, either training data or enrollment data, and the actual test data can significantly hurt text-dependent speaker verification (SV) system p…
DDTSE: Discriminative Diffusion Model for Target Speech Extraction
Leying Zhang, Yao Qian, Linfeng Yu +5
Diffusion models have gained attention in speech enhancement tasks, providing an alternative to conventional discriminative methods. However, research on target speech extraction u…
Exploring Binary Classification Loss For Speaker Verification
Bing Han, Zhengyang Chen, Yanmin Qian
The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric…
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition
Wangyou Zhang, Yanmin Qian
Self-supervised learning (SSL) based speech pre-training has attracted much attention for its capability of extracting rich representations learned from massive unlabeled data. On…
Code-Switching Text Generation and Injection in Mandarin-English ASR
Haibin Yu, Yuxuan Hu, Yao Qian +7
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling f…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
End-to-End Multi-speaker ASR with Independent Vector Analysis
Robin Scheibler, Wangyou Zhang, Xuankai Chang +2
We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition. We propose a frontend for joint source separation and dereverberation based on the in…
SkiM: Skipping Memory LSTM for Low-Latency Real-Time Continuous Speech Separation
Chenda Li, Lei Yang, Weiqin Wang +1
Continuous speech separation for meeting pre-processing has recently become a focused research topic. Compared to the data in utterance-level speech separation, the meeting-style a…
Separating Long-Form Speech with Group-Wise Permutation Invariant Training
Wangyou Zhang, Zhuo Chen, Naoyuki Kanda +8
Multi-talker conversational speech processing has drawn many interests for various applications such as meeting transcription. Speech separation is often required to handle overlap…
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions
Wangyou Zhang, Jing Shi, Chenda Li +2
The deep learning based time-domain models, e.g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement. However, many experiments on t…
Dual-Path Modeling for Long Recording Speech Separation in Meetings
Chenda Li, Zhuo Chen, Yi Luo +6
The continuous speech separation (CSS) is a task to separate the speech sources from a long, partially overlapped recording, which involves a varying number of speakers. A straight…
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Wangyou Zhang, Christoph Boeddeker, Shinji Watanabe +7
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions. However, seve…
End-to-End Speaker-Dependent Voice Activity Detection
Yefei Chen, Shuai Wang, Yanmin Qian +1
Voice activity detection (VAD) is an essential pre-processing step for tasks such as automatic speech recognition (ASR) and speaker recognition. A basic goal is to remove silent se…
Future Vector Enhanced LSTM Language Model for LVCSR
Qi Liu, Yanmin Qian, Kai Yu
Language models (LM) play an important role in large vocabulary continuous speech recognition (LVCSR). However, traditional language models only predict next single word with given…
End-to-end spoofing detection with raw waveform CLDNNs
Heinrich Dinkel, Nanxin Chen, Yanmin Qian +1
Albeit recent progress in speaker verification generates powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. Recent results in ASVSpoof2…
End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming
Wangyou Zhang, Aswin Shanmugam Subramanian, Xuankai Chang +2
Despite successful applications of end-to-end approaches in multi-channel speech recognition, the performance still degrades severely when the speech is corrupted by reverberation.…
End-to-End Multi-speaker Speech Recognition with Transformer
Xuankai Chang, Wangyou Zhang, Yanmin Qian +2
Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-ch…
MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition
Xuankai Chang, Wangyou Zhang, Yanmin Qian +2
Recently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech recognition. However, high word error rates (WERs) still prevent these systems from being…
Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition
Xu Xiang, Shuai Wang, Houjun Huang +2
Recently, speaker embeddings extracted from a speaker discriminative deep neural network (DNN) yield better performance than the conventional methods such as i-vector. In most case…