120 citations · 414 across the 61 of their papers we have counts for
23 papers · 1 filter
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI
Jinchuan Tian, Jianwei Yu, Chao Weng +4
Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI…
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Brian Yan, Chunlei Zhang, Meng Yu +6
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a genera…
SpeechMoE2: Mixture-of-Experts Model with Improved Routing
Zhao You, Shulin Feng, Dan Su +1
Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition. The design principle of router architecture is import…
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature
Yiwen Shao, Shi-Xiong Zhang, Dong Yu
Automatic speech recognition (ASR) of multi-channel multi-speaker overlapped speech remains one of the most challenging tasks to the speech community. In this paper, we look into t…
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning
Songxiang Liu, Dan Su, Dong Yu
The task of few-shot style transfer for voice cloning in text-to-speech (TTS) synthesis aims at transferring speaking styles of an arbitrary source speaker to a target speaker's vo…
Joint Neural AEC and Beamforming with Double-Talk Detection
Vinay Kothapally, Yong Xu, Meng Yu +2
Acoustic echo cancellation (AEC) in full-duplex communication systems eliminates acoustic feedback. However, nonlinear distortions induced by audio devices, background noise, rever…
FAST-RIR: Fast neural diffuse room impulse response generator
Anton Ratnarajah, Shi-Xiong Zhang, Meng Yu +3
We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RI…
Bilateral Denoising Diffusion Models
Max W. Y. Lam, Jun Wang, Rongjie Huang +2
Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models yet brought challenges to efficient sampling. In this paper, we propose novel bilater…
Referee: Towards reference-free cross-speaker style transfer with low-quality data for expressive speech synthesis
Songxiang Liu, Shan Yang, Dan Su +1
Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker's voice. Most previous CSS…
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition
Max W. Y. Lam, Jun Wang, Chao Weng +2
End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization. To extract learnable…
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts
Zhao You, Shulin Feng, Dan Su +1
Recently, Mixture of Experts (MoE) based Transformer has shown promising results in many domains. This is largely due to the following advantages of this architecture: firstly, MoE…
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition
Liqiang He, Shulin Feng, Dan Su +1
Neural architecture search (NAS) has attracted much attention and has been explored for automatic speech recognition (ASR). In this work, we focus on streaming ASR scenarios and pr…
Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation in Complex Domain
Rongzhi Gu, Shi-Xiong Zhang, Yuexian Zou +1
To date, mainstream target speech separation (TSS) approaches are formulated to estimate the complex ratio mask (cRM) of the target speech in time-frequency domain under supervised…
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
Xiyun Li, Yong Xu, Meng Yu +4
Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance ove…
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
Meng Yu, Chunlei Zhang, Yong Xu +2
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech q…
Towards Robust Speaker Verification with Target Speaker Enhancement
Chunlei Zhang, Meng Yu, Chao Weng +1
This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embeddin…
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation
Max W. Y. Lam, Jun Wang, Dan Su +1
One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throu…
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect
Jun Wang, Max W. Y. Lam, Dan Su +1
We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns…
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
Helin Wang, Bo Wu, Lianwu Chen +7
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose…
Contrastive Separative Coding for Self-supervised Representation Learning
Jun Wang, Max W. Y. Lam, Dan Su +1
To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our…
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition
Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe +2
Multi-source localization is an important and challenging technique for multi-talker conversation analysis. This paper proposes a novel supervised learning method using deep neural…
Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks
Max W. Y. Lam, Jun Wang, Dan Su +1
Recent research on the time-domain audio separation networks (TasNets) has brought great success to speech separation. Nevertheless, conventional TasNets struggle to satisfy the me…
Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation
Yong Xu, Zhuohuang Zhang, Meng Yu +2
Although the conventional mask-based minimum variance distortionless response (MVDR) could reduce the non-linear distortion, the residual noise level of the MVDR separated speech i…