39 citations · 143 across the 12 of their papers we have counts for
12 papers
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…
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training
Chengyi Wang, Yiming Wang, Yu Wu +4
Recently, masked prediction pre-training has seen remarkable progress in self-supervised learning (SSL) for speech recognition. It usually requires a codebook obtained in an unsupe…
Self-Supervised Learning for speech recognition with Intermediate layer supervision
Chengyi Wang, Yu Wu, Sanyuan Chen +4
Recently, pioneer work finds that speech pre-trained models can solve full-stack speech processing tasks, because the model utilizes bottom layers to learn speaker-related informat…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…
Continuous Speech Separation with Conformer
Sanyuan Chen, Yu Wu, Zhuo Chen +6
Continuous speech separation plays a vital role in complicated speech related tasks such as conversation transcription. The separation model extracts a single speaker signal from a…
On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Jinyu Li, Yu Wu, Yashesh Gaur +3
Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E method…