The global shift towards immersive digital experiences, from metaverse platforms to advanced virtual assistants, necessitates sophisticated voice technologies. As content creation scales and remote work becomes standard, demand for tools that deliver natural, expressive, and real-time audio is surging. This patent offers a solution to meet these evolving market demands, enabling richer interactions and more efficient content production across diverse industries.
Achieves real-time performance and high-level audio quality, previously difficult, using a unique differential spectrum method.
Enables natural synthesis that maintains original voice timbre and emotional expression through a trained conversion model and lifter.
Secured patentability against four prior art documents, providing a robust foundation for business development.
This patent protects a voice conversion device, method, and program utilizing a differential spectrum approach to achieve both real-time performance and high audio quality. The claims broadly cover hardware, software, and service implementations, indicating a robust and comprehensive protection against infringement.
While protecting core voice conversion, this patent does not explicitly cover advanced emotional AI detection or integration with specific biometric authentication systems, allowing licensees to develop complementary IP in these adjacent areas.
This technology contributes to operational efficiency by enabling voice avatars for contact center operators and reducing narration efforts in e-learning content production. For example, a company spending ~$150K (AI est.) annually on voice content production (narration, recording, editing) could save ~$50K (AI est.) per year by reducing production time by 40% with this technology. Additionally, improved customer satisfaction from enhanced automated voice response systems could prevent ~$300K (AI est.) in annual customer churn, potentially leading to a total economic impact of ~$350K (AI est.) per year.
X: Voice Expressiveness & Naturalness
Y: Real-time Processing Performance