216 citations · 298 across the 28 of their papers we have counts for
29 papers
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction
Aoqi Guo, Junnan Wu, Peng Gao +4
Recently, deep learning-based beamforming algorithms have shown promising performance in target speech extraction tasks. However, most systems do not fully utilize spatial informat…
AV-SepFormer: Cross-Attention SepFormer for Audio-Visual Target Speaker Extraction
Jiuxin Lin, Xinyu Cai, Heinrich Dinkel +7
Visual information can serve as an effective cue for target speaker extraction (TSE) and is vital to improving extraction performance. In this paper, we propose AV-SepFormer, a Sep…
Focus on the Sound around You: Monaural Target Speaker Extraction via Distance and Speaker Information
Jiuxin Lin, Peng Wang, Heinrich Dinkel +6
Previously, Target Speaker Extraction (TSE) has yielded outstanding performance in certain application scenarios for speech enhancement and source separation. However, obtaining au…
Exploring Representation Learning for Small-Footprint Keyword Spotting
Fan Cui, Liyong Guo, Quandong Wang +2
In this paper, we investigate representation learning for low-resource keyword spotting (KWS). The main challenges of KWS are limited labeled data and limited available device reso…
Relate auditory speech to EEG by shallow-deep attention-based network
Fan Cui, Liyong Guo, Lang He +4
Electroencephalography (EEG) plays a vital role in detecting how brain responses to different stimulus. In this paper, we propose a novel Shallow-Deep Attention-based Network (SDAN…
Improving Weakly Supervised Sound Event Detection with Causal Intervention
Yifei Xin, Dongchao Yang, Fan Cui +2
Existing weakly supervised sound event detection (WSSED) work has not explored both types of co-occurrences simultaneously, i.e., some sound events often co-occur, and their occurr…
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding
Fengyu Yang, Jian Luan, Yujun Wang
In most cases, bilingual TTS needs to handle three types of input scripts: first language only, second language only, and second language embedded in the first language. In the lat…
An empirical study of weakly supervised audio tagging embeddings for general audio representations
Heinrich Dinkel, Zhiyong Yan, Yongqing Wang +2
We study the usability of pre-trained weakly supervised audio tagging (AT) models as feature extractors for general audio representations. We mainly analyze the feasibility of tran…
UniKW-AT: Unified Keyword Spotting and Audio Tagging
Heinrich Dinkel, Yongqing Wang, Zhiyong Yan +2
Within the audio research community and the industry, keyword spotting (KWS) and audio tagging (AT) are seen as two distinct tasks and research fields. However, from a technical po…
Pseudo strong labels for large scale weakly supervised audio tagging
Heinrich Dinkel, Zhiyong Yan, Yongqing Wang +2
Large-scale audio tagging datasets inevitably contain imperfect labels, such as clip-wise annotated (temporally weak) tags with no exact on- and offsets, due to a high manual label…
Learning Decoupling Features Through Orthogonality Regularization
Li Wang, Rongzhi Gu, Weiji Zhuang +3
Keyword spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. Research shows that the state-of-art KWS and SV models are trained independentl…
Improving Emotional Speech Synthesis by Using SUS-Constrained VAE and Text Encoder Aggregation
Fengyu Yang, Jian Luan, Yujun Wang
Learning emotion embedding from reference audio is a straightforward approach for multi-emotion speech synthesis in encoder-decoder systems. But how to get better emotion embedding…
PAMA-TTS: Progression-Aware Monotonic Attention for Stable Seq2Seq TTS With Accurate Phoneme Duration Control
Yunchao He, Jian Luan, Yujun Wang
Sequence expansion between encoder and decoder is a critical challenge in sequence-to-sequence TTS. Attention-based methods achieve great naturalness but suffer from unstable issue…
A Separable Temporal Convolution Neural Network with Attention for Small-Footprint Keyword Spotting
Shenghua Hu, Jing Wang, Yujun Wang +2
Keyword spotting (KWS) on mobile devices generally requires a small memory footprint. However, most current models still maintain a large number of parameters in order to ensure go…
Separable Temporal Convolution plus Temporally Pooled Attention for Lightweight High-performance Keyword Spotting
Shenghua Hu, Jing Wang, Yujun Wang +1
Keyword spotting (KWS) on mobile devices generally requires a small memory footprint. However, most current models still maintain a large number of parameters in order to ensure go…
Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model
Quandong Wang, Junnan Wu, Zhao Yan +6
We propose a multi-channel speech enhancement approach with a novel two-stage feature fusion method and a pre-trained acoustic model in a multi-task learning paradigm. In the first…
Msdtron: a high-capability multi-speaker speech synthesis system for diverse data using characteristic information
Qinghua Wu, Quanbo Shen, Jian Luan +1
In multi-speaker speech synthesis, data from a number of speakers usually tend to have great diversity due to the fact that the speakers may differ largely in ages, speaking styles…
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
Guoguo Chen, Shuzhou Chai, Guanbo Wang +18
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and…
speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment
Junbo Zhang, Zhiwen Zhang, Yongqing Wang +6
This paper introduces a new open-source speech corpus named "speechocean762" designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native sp…
Multi-Channel Automatic Speech Recognition Using Deep Complex Unet
Yuxiang Kong, Jian Wu, Quandong Wang +4
The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverbe…
AutoKWS: Keyword Spotting with Differentiable Architecture Search
Bo Zhang, Wenfeng Li, Qingyuan Li +3
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accur…
Exploiting Deep Sentential Context for Expressive End-to-End Speech Synthesis
Fengyu Yang, Shan Yang, Qinghua Wu +2
Attention-based seq2seq text-to-speech systems, especially those use self-attention networks (SAN), have achieved state-of-art performance. But an expressive corpus with rich proso…
RawNet: Fast End-to-End Neural Vocoder
Yunchao He, Yujun Wang
Neural network-based vocoders have recently demonstrated the powerful ability to synthesize high-quality speech. These models usually generate samples by conditioning on spectral f…
End-to-end Models with auditory attention in Multi-channel Keyword Spotting
Haitong Zhang, Junbo Zhang, Yujun Wang
In this paper, we propose an attention-based end-to-end model for multi-channel keyword spotting (KWS), which is trained to optimize the KWS result directly. As a result, our model…