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. |
| Outcome: | The proposed model can recognize which language is given as input speech . it can accurately recognize speech in noisy environments, such as crowded restaurants . |
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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. |
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| Challenge: | AVLM integrates full-face visual cues into a pre-trained expressive speech model. |
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| Challenge: | Prior work in multilingual and cross-lingual speech recognition has been limited to a subset of the world's most-spoken languages. |
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| Challenge: | Existing methods to synthesize speech for low-resource languages require a substantial amount of source language corpora to generate the linguistic knowledge that can be reused for speech synthesis. |
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| Challenge: | Current automatic speech recognition systems show remarkable performance when adequate data is used for training. |
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| Challenge: | Current state-of-the-art acoustic models can easily comprise more than 100 million parameters. |
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
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Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut (2022.lrec-1)
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| Challenge: | Using acoustic data, we develop automatic speech recognition systems for three low resource languages. |
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Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives (2024.findings-acl)
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Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu
| Challenge: | Existing video-language understanding systems with human-like senses can mimic both our linguistic medium and visual environment with temporal dynamics. |
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