Xiaoda Yang, Xize Cheng, Jiaqi Duan, Hongshun Qiu, Minjie Hong, Minghui Fang, Shengpeng Ji, Jialong Zuo, Zhiqing Hong, Zhimeng Zhang, Tao Jin
| Challenge: | Recent work has shown poor performance with non-Indo-European languages . previous work primarily utilizes video information to build VSR models . |
| Approach: | They propose a generative model for data inflation that integrates synthetic data with authentic visual data to enhance the VSR model. |
| Outcome: | The proposed model improves on the audio-visual alignment problem in audio-video tasks. |
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| Challenge: | Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. |
| Approach: | They propose a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art by 18.5% WER and 4.7 BLEU on downstream audio-visual speech recognition and translation tasks. |
AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)
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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao
| Challenge: | Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech. |
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Multimodal and Multiresolution Speech Recognition with Transformers (2020.acl-main)
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| Challenge: | Existing audio visual automatic speech recognition systems rely on audio input to produce transcriptions. |
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On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
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Intuitive Multilingual Audio-Visual Speech Recognition with a Single-Trained Model (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual models outperform monolingual ones. |
| Approach: | They propose a single model that can capture which language is given as input speech . they use a pre-trained model to fine-tune the model so it can recognize the language class as well as the speech with the corresponding language. |
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Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition (2023.acl-long)
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| Challenge: | Existing efforts to improve robustness of audio-visual speech recognition with visual information focus on audio modality . current approaches introduce noise adaptation techniques to improve reliability of AVSR task . |
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Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation (2025.findings-emnlp)
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| Challenge: | AVLM integrates full-face visual cues into a pre-trained expressive speech model. |
| Approach: | They propose an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model. |
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The Effects of Pretraining in Video-Guided Machine Translation (2024.lrec-main)
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| Challenge: | Existing approaches to improve VMT models integrate text and video modalities. |
| Approach: | They propose an approach that improves the performance of VMT models by using a new dataset which contains transcribed audio descriptions of movies. |
| Outcome: | The proposed model improves on the MAD (Movie Audio Descriptions) dataset. |
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding (2023.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) are capable of understanding multi-modal content, but textonly human-computer interaction is not sufficient for many application scenarios. |
| Approach: | They propose a video-to-text generation task and a multi-modal framework that bootstraps cross-modal training from frozen pre-trained visual & audio encoders and frozen LLMs. |
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