6 papers
RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
In real-time speech communication systems, speech signals are often degraded by multiple distortions. Recently, a two-stage Repair-and-Denoising network (RaD-Net) was proposed with…
BS-PLCNet 2: Two-stage Band-split Packet Loss Concealment Network with Intra-model Knowledge Distillation
Zihan Zhang, Xianjun Xia, Chuanzeng Huang +2
Audio packet loss is an inevitable problem in real-time speech communication. A band-split packet loss concealment network (BS-PLCNet) targeting full-band signals was recently prop…
RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-s…
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators
Zihan Zhang, Jiayao Sun, Xianjun Xia +3
Packet loss is a common and unavoidable problem in voice over internet phone (VoIP) systems. To deal with the problem, we propose a band-split packet loss concealment network (BS-P…
Language-universal phonetic encoder for low-resource speech recognition
Siyuan Feng, Ming Tu, Rui Xia +2
Multilingual training is effective in improving low-resource ASR, which may partially be explained by phonetic representation sharing between languages. In end-to-end (E2E) ASR sys…
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition
Siyuan Feng, Ming Tu, Rui Xia +2
We improve low-resource ASR by integrating the ideas of multilingual training and self-supervised learning. Concretely, we leverage an International Phonetic Alphabet (IPA) multili…