arXiv:2606.30356v1 Announce Type: cross Abstract: We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that jointly optimizes analysis and synthesis objectives. OLIVE combines view-augmented masked latent prediction with waveform reconstruction under a unified objective. Reconstruction constrains early encoder features to retain signal-level information, while masked latent prediction shapes later contextual representations toward invariance for robust downstream performance. We show that these objective
The continuous advancements in AI and machine learning drive the development of more efficient and robust self-supervised learning frameworks for complex data types like speech.
This research introduces a novel approach to self-supervised speech representation learning, potentially leading to more accurate and robust voice technologies critical for various applications.
The proposed OLIVE framework combines analysis and synthesis objectives, optimizing waveform reconstruction alongside masked latent prediction, which could enhance the efficiency and performance of speech AI models.
- · AI research institutions
- · Speech technology companies
- · Developers of voice assistants
- · Companies in natural language processing
- · Companies relying on less efficient speech learning models
- · Research groups with suboptimal self-supervised learning methods
Improved performance and accuracy across various speech AI applications, including voice recognition and synthesis.
Reduced need for large labeled datasets in speech AI, accelerating development cycles and deployment.
Enhanced human-computer interaction through more natural and reliable voice interfaces, potentially impacting industries from customer service to healthcare.
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Read at arXiv cs.LG