Nvidia corporation (20240161728). SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Subhankar Ghosh of Santa Clara CA (US)
Boris Ginsburg of Sunnyvale CA (US)
SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161728 titled 'SYNTHETIC SPEECH GENERATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Simplified Explanation
The patent application describes techniques for generating artificial speech using machine learning, including obtaining synthetic embeddings and generating synthetic audio data.
- Obtaining synthetic embeddings using learned embeddings associated with different speakers.
- Generating at least one learned embedding using a multi-stage training of a machine learning model with progressively increasing quality of training speech utterances.
- Using the machine learning model and the synthetic embedding to generate synthetic audio data.
Potential Applications
This technology could be applied in various fields such as virtual assistants, customer service chatbots, language translation services, and audio book narration.
Problems Solved
This technology solves the problem of creating natural-sounding artificial speech, which is crucial for applications like virtual assistants and language translation services to enhance user experience.
Benefits
The benefits of this technology include improved speech synthesis quality, personalized speech generation based on different speakers, and the ability to generate synthetic audio data efficiently.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of voice-enabled devices and services, such as smart speakers, voice-controlled appliances, and interactive voice response systems.
Possible Prior Art
Prior art in the field of speech synthesis and machine learning techniques for generating artificial speech may include research papers, patents, and existing products or services utilizing similar technologies.
Unanswered Questions
How does this technology compare to existing speech synthesis methods?
This article does not provide a direct comparison with existing speech synthesis methods in terms of performance, efficiency, or accuracy.
What are the limitations of this technology in terms of scalability and real-time applications?
The article does not address the scalability of the technology for processing large amounts of data or its suitability for real-time applications where low latency is critical.
Original Abstract Submitted
disclosed are apparatuses, systems, and techniques that may use machine learning for generating artificial speech. the techniques include obtaining a synthetic embedding using learned embeddings associated with different speakers. at least one learned embedding may be generated using a multi-stage training of a machine learning model (mlm) with progressively increasing quality of training speech utterances. the techniques may further include using the mlm and the synthetic embedding to generate synthetic audio data.
