NVIDIA Corporation (20240233714). HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract
Contents
HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Vladimir Bataev of Yerevan (AM)
Roman Korostik of Yerevan (AM)
Evgenii Shabalin of Moskva (RU)
Vitaly Sergeyevich Lavrukhin of Campbell CA (US)
Boris Ginsburg of Sunnyvale CA (US)
HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240233714 titled 'HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
The abstract of the patent application describes a process where textual data is converted into an intermediate speech representation using machine learning models. This representation is then used with another machine learning model to generate output data indicating a different set of textual data. The parameters of the second machine learning model are updated using the output data and ground truth data associated with the original textual data. Additionally, a generative adversarial network is used to enhance the initial audio representation before providing it to the second machine learning model.
- Textual data is converted into an intermediate speech representation using machine learning models.
- The intermediate representation is used with another machine learning model to generate output data indicating a different set of textual data.
- Parameters of the second machine learning model are updated using the output data and ground truth data associated with the original textual data.
- A generative adversarial network is used to enhance the initial audio representation before providing it to the second machine learning model.
Potential Applications: - Speech recognition technology - Text-to-speech applications - Audio enhancement tools
Problems Solved: - Improving accuracy and efficiency of speech recognition systems - Enhancing the quality of text-to-speech conversions - Providing a more seamless integration between textual data and audio representations
Benefits: - Enhanced user experience in speech-related applications - Increased accuracy in converting text to speech - Improved overall performance of speech recognition systems
Commercial Applications: Title: Advanced Speech Recognition and Text-to-Speech Technology This technology can be utilized in various industries such as: - Customer service - Healthcare - Education - Entertainment
Questions about Advanced Speech Recognition and Text-to-Speech Technology: 1. How does this technology improve the accuracy of speech recognition systems? 2. What are the potential commercial applications of this technology?
Frequently Updated Research: Stay updated on the latest advancements in speech recognition technology and text-to-speech applications to leverage the benefits of this innovative solution.
Original Abstract Submitted
in various examples, first textual data may be applied to a first mlm to generate an intermediate speech representation (e.g., a frequency-domain representation), the intermediate audio representation and a second mlm may be used to generate output data indicating second textual data, and parameters of the second mlm may be updated using the output data and ground truth data associated with the first textual data. the first mlm may include a trained text-to-speech (tts) model and the second mlm may include an automatic speech recognition (asr) model. a generator from a generative adversarial networks may be used to enhance an initial intermediate audio representation generated using the first mlm and the enhanced intermediate audio representation may be provided to the second mlm. the generator may include generator blocks that receive the initial intermediate audio representation to sequentially generate the enhanced intermediate audio representation.
