271 citations · 472 across the 14 of their papers we have counts for
4 papers · 1 filter
LiMuSE: Lightweight Multi-modal Speaker Extraction
Qinghua Liu, Yating Huang, Yunzhe Hao +2
Multi-modal cues, including spatial information, facial expression and voiceprint, are introduced to the speech separation and speaker extraction tasks to serve as complementary in…
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
Linghui Meng, Jin Xu, Xu Tan +3
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking…
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition
Cheng Yi, Shiyu Zhou, Bo Xu
End-to-end models have achieved impressive results on the task of automatic speech recognition (ASR). For low-resource ASR tasks, however, labeled data can hardly satisfy the deman…
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
Cheng Yi, Jianzhong Wang, Ning Cheng +2
There are several domains that own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of unlabeled…