1.9k citations · 2.4k across the 69 of their papers we have counts for
14 papers · 1 filter
EchoChat: Structured Cognitive Reasoning in Empathetic Spoken Dialogue
Dingdong Wang, Shujie Liu, Yayue Deng +6
Empathetic spoken dialogue is a sophisticated cognitive process that requires not only recognizing emotions but also inferring a user's latent mental states to provide appropriate…
TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation
Qinzhe Hu, Chenda Li, Wangyou Zhang +3
Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment…
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling
Jiacheng Shi, Hongfei Du, Xinyuan Song +3
Neural speech codecs provide discrete representations for speech language models, but emotional cues are often degraded during quantization. Existing codecs mainly optimize acousti…
Investigating Neural Audio Codecs for Speech Language Model-Based Speech Generation
Jiaqi Li, Dongmei Wang, Xiaofei Wang +13
Neural audio codec tokens serve as the fundamental building blocks for speech language model (SLM)-based speech generation. However, there is no systematic understanding on how the…
Advanced Long-Content Speech Recognition With Factorized Neural Transducer
Xun Gong, Yu Wu, Jinyu Li +4
In this paper, we propose two novel approaches, which integrate long-content information into the factorized neural transducer (FNT) based architecture in both non-streaming (refer…
WavMark: Watermarking for Audio Generation
Guangyu Chen, Yu Wu, Shujie Liu +3
Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice using just a few seconds of recording while maintaining a high level of realism. Alongsid…
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer
Xun Gong, Yu Wu, Jinyu Li +4
Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is mor…
Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation
Kun Wei, Long Zhou, Ziqiang Zhang +5
Direct speech-to-speech translation (S2ST) is an attractive research topic with many advantages compared to cascaded S2ST. However, direct S2ST suffers from the data scarcity probl…
Speech Pre-training with Acoustic Piece
Shuo Ren, Shujie Liu, Yu Wu +2
Previous speech pre-training methods, such as wav2vec2.0 and HuBERT, pre-train a Transformer encoder to learn deep representations from audio data, with objectives predicting eithe…
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data
Junyi Ao, Ziqiang Zhang, Long Zhou +7
This paper studies a novel pre-training technique with unpaired speech data, Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within a multi-task learning fr…
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Optimizing Alignment of Speech and Language Latent Spaces for End-to-End Speech Recognition and Understanding
Wei Wang, Shuo Ren, Yao Qian +4
The advances in attention-based encoder-decoder (AED) networks have brought great progress to end-to-end (E2E) automatic speech recognition (ASR). One way to further improve the pe…
Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification
Zhengyang Chen, Sanyuan Chen, Yu Wu +5
The speech representations learned from large-scale unlabeled data have shown better generalizability than those from supervised learning and thus attract a lot of interest to be a…
Don't shoot butterfly with rifles: Multi-channel Continuous Speech Separation with Early Exit Transformer
Sanyuan Chen, Yu Wu, Zhuo Chen +3
With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been…