arXiv:2606.08843v1 Announce Type: cross Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning. The retrieved segments serve as synthetic inputs, while real target audio provides ground-truth outputs, forming a synthetic-to-real training paradigm that naturally supports multilingual data without requiring parallel corpora or explicit alignment. To ensure consistent target-speaker identity, we incorporate a spe
The continuous advancements in AI and deep learning provide the necessary technical foundation for zero-shot voice conversion across diverse languages without parallel data.
This development significantly lowers the barrier for creating synthetic speech in multiple languages, enabling more natural and accessible human-computer interaction and content generation.
The need for extensive, parallel training datasets for voice conversion is reduced, allowing for rapid deployment across new languages and scenarios previously restricted by data availability.
- · AI voice synthesis companies
- · Multilingual content creators
- · Personalized AI assistant developers
- · Accessibility technology providers
- · Companies relying on expensive parallel dataset acquisition
- · Traditional voice acting in certain applications
More realistic and diverse synthetic voice options become widely available for various applications.
Increased adoption of AI-generated speech in media, customer service, and educational platforms, expanding global reach.
Potential ethical and regulatory challenges arise concerning identity theft, deepfakes, and the authenticity of recorded speech.
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Read at arXiv cs.LG