Nvidia corporation (20240135920). HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract

HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

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 20240135920 titled 'HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Simplified Explanation

The abstract describes a method where textual data is converted to speech using machine learning models, with the output data then used to update parameters of the models. A generator from a generative adversarial network is used to enhance the audio representation before passing it to the speech recognition model.

• Machine learning models are used to convert textual data to speech representations.
• A generative adversarial network is employed to enhance the audio representation.
• The output data is used to update parameters of the models.

Potential Applications

This technology could be applied in:

• Speech synthesis
• Voice recognition systems
• Language translation tools

Problems Solved

This technology helps in:

• Improving speech recognition accuracy
• Enhancing the quality of synthesized speech
• Streamlining the process of converting text to speech

Benefits

The benefits of this technology include:

• Increased efficiency in speech synthesis
• Improved accuracy in speech recognition
• Enhanced user experience in voice-controlled devices

Potential Commercial Applications

This technology could be utilized in:

• Virtual assistants
• Call center automation
• Language learning applications

Possible Prior Art

One possible prior art could be the use of machine learning models in speech recognition and synthesis, which has been a growing field in recent years.

Unanswered Questions

How does the generator from the generative adversarial network enhance the audio representation?

The abstract mentions the use of a generator to enhance the audio representation, but it does not provide specific details on the mechanism or process involved in this enhancement.

What are the specific parameters that are updated using the output data?

While the abstract mentions that the output data is used to update parameters of the models, it does not specify which parameters are being updated or how this updating process occurs.

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.