Papers with noisy speech
Learning Robust and Multilingual Speech Representations (2020.findings-emnlp)
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| Challenge: | Unsupervised speech representation learning has shown success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. |
| Approach: | They evaluate unsupervised speech representation learning representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. |
| Outcome: | The proposed representations improve the recognition performance in 25 phonetically diverse languages and are robust to domain shifts. |
Towards Noise-Tolerant Speech-Referring Video Object Segmentation: Bridging Speech and Text (2023.emnlp-main)
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| Challenge: | Recent advances in vision-language learning have significantly advanced Human-Computer Interactions (HCI). |
| Approach: | They propose a method to align the semantic spaces between speech and text by incorporating two modules to align semantic spaces. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on AVOS benchmarks. |
CIS-BWE: Chaos-Informed Speech Bandwidth Extension (2026.acl-long)
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| Challenge: | CIS-BWE introduces two chaos-informed discriminators for capturing the deterministic chaos from speech. |
| Approach: | They propose a novel adversarial Bandwidth Extension framework that introduces two chaos-informed discriminators for capturing the deterministic chaos from speech. |
| Outcome: | The proposed framework achieves better performance across nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task. |