Dell Products L.P. (20240242705). METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING simplified abstract
Contents
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING
Organization Name
Inventor(s)
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242705 titled 'METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING
The method described in the abstract involves determining multiple loss functions for different sub-models of a speech generation model based on processed feature vectors, and updating the model parameters accordingly.
- The method determines a first loss function for a first sub-model, a second loss function for a second sub-model, a third loss function for a third sub-model, and a fourth loss function for a fourth sub-model of the speech generation model.
- The loss functions are based on feature vectors associated with training image, audio, and text information used to train the model.
- Parameters of the speech generation model are updated based on the calculated loss functions.
Potential Applications: - Speech synthesis technology - Natural language processing - Voice assistants and chatbots
Problems Solved: - Enhancing the accuracy and performance of speech generation models - Improving the quality of synthesized speech
Benefits: - More realistic and natural-sounding speech output - Better user experience with voice-enabled applications - Increased efficiency in speech synthesis processes
Commercial Applications: Title: Advanced Speech Generation Technology for Improved User Interaction This technology can be utilized in various industries such as: - Customer service - Entertainment (e.g., virtual assistants in gaming) - Accessibility tools for individuals with speech impairments
Questions about the technology: 1. How does this method improve the overall performance of speech generation models? - The method optimizes the loss functions for different sub-models, leading to more accurate and natural speech synthesis. 2. What are the key factors that influence the effectiveness of this approach? - The quality and diversity of the training data, as well as the design of the sub-models, play crucial roles in the success of this method.
Original Abstract Submitted
a method in an illustrative embodiment includes determining a first loss function for a first sub-model of a speech generation model based on a plurality of feature vectors associated with training image information, training audio information, and training text information used to train the speech generation model. the method may further include determining a second loss function for a second sub-model and a third loss function for a third sub-model of the speech generation model based on the plurality of feature vectors that have been processed. in addition, the method may further include determining a fourth loss function for a fourth sub-model of the speech generation model based on the processed plurality of feature vectors. the method may further include updating parameters of the speech generation model based on the first loss function, the second loss function, the third loss function, and the fourth loss function.
