رؤى - Speech Technology - # Cross-Lingual Audio-Visual Speech Representation

XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception

Q: How does XLAVS-R address the challenge of limited multilingual AV pre-training data?

XLAVS-R addresses the challenge of limited multilingual AV pre-training data by leveraging audio-only data, which is more abundant and easier to scale compared to audio-visual data. The model first undergoes audio-only pre-training, utilizing a large volume of speech data in multiple languages. Then, XLAVS-R introduces the visual modality by continuing training with audio-visual self-supervised learning using contextualized representations from the initial audio-only pre-training stage. This approach maximizes the efficiency of multi-lingual AV pre-training by injecting visual signals after establishing a strong foundation through audio-only training.

Q: What are the implications of XLAVS-R's improved training efficiency on future research in speech technology?

The improved training efficiency demonstrated by XLAVS-R has significant implications for future research in speech technology: Scalability: By simplifying existing pre-training schemes and enhancing training protocols, XLAVS-R showcases a more efficient way to leverage both audio-only and audio-visual data for robust speech perception across multiple languages. Robustness: The model's ability to achieve state-of-the-art performance on downstream tasks even with noisy inputs highlights its robustness, paving the way for more resilient speech recognition systems in real-world environments. Zero-shot Learning: XLAVS-R's success in zero-shot transfer learning from AV representation to A-only fine-tuned models opens up possibilities for reducing dependency on labeled AV datasets for downstream tasks, potentially lowering barriers to entry for new applications or low-resource languages.

Q: How can the concept of zero-shot ability be applied beyond the scope of this study?

The concept of zero-shot ability demonstrated by XLAVS-R can be applied beyond this study in various ways: Multimodal Tasks: Zero-shot learning can be utilized in other multimodal tasks such as image captioning or video understanding where models trained on one modality can adapt effectively to another without explicit supervision. Cross-domain Transfer: Zero-shot techniques can facilitate knowledge transfer between different domains within natural language processing (NLP), computer vision, or other AI fields without requiring task-specific labeled datasets. Low-resource Settings: In scenarios with limited annotated data, zero-shot approaches enable models pretrained on rich resources to generalize well to unseen classes or languages during inference time. Adaptation Across Modalities: Beyond just text-to-speech applications like those explored here, zero-shot abilities could extend into areas like sentiment analysis combining text and images or medical diagnosis merging clinical notes with patient scans. By applying these principles creatively across diverse domains and modalities, researchers can unlock new avenues for efficient model adaptation and generalization without extensive manual labeling efforts.

المفاهيم الأساسية

XLAVS-R enhances noise-robust speech perception through cross-lingual audio-visual representation learning.

الملخص

The content introduces XLAVS-R, a model for noise-robust speech recognition and translation in over 100 languages. It leverages limited multilingual AV pre-training data to improve robustness to noise. The model outperforms previous state-of-the-art by up to 18.5% WER and 4.7 BLEU in noisy AV inputs, enabling strong zero-shot audio-visual ability with audio-only fine-tuning.

Introduction
- Speech recognition and translation challenges in noisy environments.
- Importance of augmenting systems with visual signals.
Data Extraction
- "XLAVS-R exploits audio-only speech data for efficient data scaling and language coverage expansion."
- "XLAVS-R improves training efficiency by single-round training with unit targets from audio-only contextualized representation."
Related Work
- Self-supervised audio-only speech representation.
- Self-supervised audio-visual speech representation.
Experiments
- Evaluation on MuAViC benchmark for AVSR and AVS2TT tasks.
- Comparison of XLAVS-R models against baselines in clean and noisy settings.
Results
- Multilingual Speech Recognition: XLAVS-R outperforms baselines in both clean and noisy settings across multiple languages.
- Multilingual Speech-To-Text Translation: XLAVS-R excels in translation tasks, showing significant improvements over baselines.
Ablation Experiments of XLAVS-R
- Validation of key changes from AV-HuBERT to XLAVS-R, showcasing the effectiveness of each component step-by-step.
Zero-shot Audio-Visual Inference
- Comparison between A-only fine-tuning vs. AV fine-tuning, demonstrating the zero-shot ability of XLAVS-R models.

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arxiv.org

الإحصائيات

"XLAVS-R exploits audio-only speech data for efficient data scaling and language coverage expansion."
"XLAVS-R improves training efficiency by single-round training with unit targets from audio-only contextualized representation."

اقتباسات

"XLAVS-R yields SOTA performance on downstream audio-visual speech recognition and translation tasks."
"XLAVS-R effectively leverages unlabeled audio-only multilingual speech data for enhanced zero-shot audio-visual ability."

الرؤى الأساسية المستخلصة من

XLAVS-R

by HyoJung Han,... في arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14402.pdf

استفسارات أعمق

How does XLAVS-R address the challenge of limited multilingual AV pre-training data?

XLAVS-R addresses the challenge of limited multilingual AV pre-training data by leveraging audio-only data, which is more abundant and easier to scale compared to audio-visual data. The model first undergoes audio-only pre-training, utilizing a large volume of speech data in multiple languages. Then, XLAVS-R introduces the visual modality by continuing training with audio-visual self-supervised learning using contextualized representations from the initial audio-only pre-training stage. This approach maximizes the efficiency of multi-lingual AV pre-training by injecting visual signals after establishing a strong foundation through audio-only training.

What are the implications of XLAVS-R's improved training efficiency on future research in speech technology?

The improved training efficiency demonstrated by XLAVS-R has significant implications for future research in speech technology:

Scalability: By simplifying existing pre-training schemes and enhancing training protocols, XLAVS-R showcases a more efficient way to leverage both audio-only and audio-visual data for robust speech perception across multiple languages.
Robustness: The model's ability to achieve state-of-the-art performance on downstream tasks even with noisy inputs highlights its robustness, paving the way for more resilient speech recognition systems in real-world environments.
Zero-shot Learning: XLAVS-R's success in zero-shot transfer learning from AV representation to A-only fine-tuned models opens up possibilities for reducing dependency on labeled AV datasets for downstream tasks, potentially lowering barriers to entry for new applications or low-resource languages.

How can the concept of zero-shot ability be applied beyond the scope of this study?

The concept of zero-shot ability demonstrated by XLAVS-R can be applied beyond this study in various ways:

Multimodal Tasks: Zero-shot learning can be utilized in other multimodal tasks such as image captioning or video understanding where models trained on one modality can adapt effectively to another without explicit supervision.
Cross-domain Transfer: Zero-shot techniques can facilitate knowledge transfer between different domains within natural language processing (NLP), computer vision, or other AI fields without requiring task-specific labeled datasets.
Low-resource Settings: In scenarios with limited annotated data, zero-shot approaches enable models pretrained on rich resources to generalize well to unseen classes or languages during inference time.
Adaptation Across Modalities: Beyond just text-to-speech applications like those explored here, zero-shot abilities could extend into areas like sentiment analysis combining text and images or medical diagnosis merging clinical notes with patient scans.

By applying these principles creatively across diverse domains and modalities, researchers can unlock new avenues for efficient model adaptation and generalization without extensive manual labeling efforts.