Search Results for author: Wenxin Hou

Found 9 papers, 7 papers with code

Boosting Cross-Domain Speech Recognition with Self-Supervision

no code implementations20 Jun 2022 Han Zhu, Gaofeng Cheng, Jindong Wang, Wenxin Hou, Pengyuan Zhang, Yonghong Yan

The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions.

Automatic Speech Recognition Pseudo Label +3

Margin Calibration for Long-Tailed Visual Recognition

no code implementations14 Dec 2021 Yidong Wang, BoWen Zhang, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki

The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.

FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

1 code implementation NeurIPS 2021 BoWen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki

However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.

Semi-Supervised Image Classification

Exploiting Adapters for Cross-lingual Low-resource Speech Recognition

2 code implementations18 May 2021 Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, Takahiro Shinozaki

Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.

Cross-Lingual ASR General Knowledge +3

Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning

1 code implementation25 Oct 2020 Wenxin Hou, Yue Dong, Bairong Zhuang, Longfei Yang, Jiatong Shi, Takahiro Shinozaki

In this paper, we report a large-scale end-to-end language-independent multilingual model for joint automatic speech recognition (ASR) and language identification (LID).

Automatic Speech Recognition Language Identification +2

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