Sanas.ai Inc. (20240265908). METHODS FOR REAL-TIME ACCENT CONVERSION AND SYSTEMS THEREOF simplified abstract
Contents
METHODS FOR REAL-TIME ACCENT CONVERSION AND SYSTEMS THEREOF
Organization Name
Inventor(s)
Maxim Serebryakov of Palo Alto CA (US)
Shawn Zhang of Pleasanton CA (US)
METHODS FOR REAL-TIME ACCENT CONVERSION AND SYSTEMS THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265908 titled 'METHODS FOR REAL-TIME ACCENT CONVERSION AND SYSTEMS THEREOF
The abstract describes techniques for real-time accent conversion using machine learning algorithms.
- Example computing device receives indications of two accents and speech content with the first accent.
- The device uses a machine learning algorithm to derive a linguistic representation of the speech content with the first accent.
- Based on this representation, the device synthesizes audio data with the second accent using another machine learning algorithm.
- The synthesized audio data is then converted into a version of the speech content with the second accent.
Potential Applications: - Language learning tools - Virtual assistants with different accents - Improving speech recognition systems
Problems Solved: - Facilitates communication between individuals with different accents - Enhances user experience in speech-based applications
Benefits: - Increased accessibility and inclusivity in communication - Improved accuracy in speech recognition and synthesis
Commercial Applications: Accent conversion technology can be utilized in language learning apps, customer service chatbots, and virtual reality simulations to enhance user experience and facilitate cross-cultural communication.
Questions about Accent Conversion: 1. How does accent conversion technology impact language learning tools? Accent conversion technology can enhance language learning tools by providing learners with exposure to different accents and improving their ability to understand and communicate with speakers of various accents.
2. What are the potential challenges in implementing accent conversion technology in real-time communication applications? Implementing accent conversion technology in real-time communication applications may face challenges related to processing speed, accuracy, and adapting to various accents seamlessly.
Original Abstract Submitted
techniques for real-time accent conversion are described herein. an example computing device receives an indication of a first accent and a second accent. the computing device further receives, via at least one microphone, speech content having the first accent. the computing device is configured to derive, using a first machine-learning algorithm trained with audio data including the first accent, a linguistic representation of the received speech content having the first accent. the computing device is configured to, based on the derived linguistic representation of the received speech content having the first accent, synthesize, using a second machine learning-algorithm trained with (i) audio data comprising the first accent and (ii) audio data including the second accent, audio data representative of the received speech content having the second accent. the computing device is configured to convert the synthesized audio data into a synthesized version of the received speech content having the second accent.
