Nvidia corporation (20240127788). CUSTOMIZING TEXT-TO-SPEECH LANGUAGE MODELS USING ADAPTERS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract

CUSTOMIZING TEXT-TO-SPEECH LANGUAGE MODELS USING ADAPTERS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Cheng-Ping Hsieh of La Jolla CA (US)

Subhankar Ghosh of Santa Clara CA (US)

Boris Ginsburg of Sunnyvale CA (US)

CUSTOMIZING TEXT-TO-SPEECH LANGUAGE MODELS USING ADAPTERS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240127788 titled 'CUSTOMIZING TEXT-TO-SPEECH LANGUAGE MODELS USING ADAPTERS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Simplified Explanation

The abstract describes a method for customizing or adapting text-to-speech machine learning models to accommodate new or additional speakers without requiring a full re-training of the models. This involves adding adapter layers to the base model and updating parameters to generate an adapted model that can support both the original speakers and the new speakers.

• One way to customize text-to-speech machine learning models for new speakers is by adding adapter layers to the base model.
• The adapter layers allow the model to be updated without re-training the entire model, making it easier to support new or additional speakers.

Potential Applications

This technology could be applied in various fields such as:

• Language learning applications
• Virtual assistants
• Audiobook production

Problems Solved

This technology solves the following problems:

• Streamlining the process of adding new speakers to text-to-speech models
• Improving the accuracy and naturalness of synthesized speech for different speakers

Benefits

The benefits of this technology include:

• Faster adaptation of text-to-speech models for new speakers
• Enhanced flexibility in supporting multiple speaker voices
• Improved user experience in applications utilizing synthesized speech

Potential Commercial Applications

The potential commercial applications of this technology include:

• Speech synthesis software for businesses
• Voice-enabled devices and applications
• Customer service chatbots

Possible Prior Art

One possible prior art in this field is the use of transfer learning techniques to adapt machine learning models for new tasks or datasets. Another could be the development of speaker adaptation methods in speech recognition systems.

How does this method compare to traditional re-training approaches for text-to-speech models?

This method allows for faster adaptation to new speakers without the need for re-training the entire model, but how does it impact the overall performance compared to traditional methods?

What are the limitations of using adapter layers in text-to-speech models for speaker adaptation?

While adapter layers provide a way to customize models for new speakers, are there any constraints or drawbacks to this approach that need to be considered?

Original Abstract Submitted

in various examples, one or more text-to-speech machine learning models may be customized or adapted to accommodate new or additional speakers or speaker voices without requiring a full re-training of the models. for example, a base model may be trained on a set of one or more speakers and, after training or deployment, the model may be adapted to support one or more other speakers. to do this, one or more additional layers (e.g., adapter layers) may be added to the model, and the model may be re-trained or updated—e.g., by freezing parameters of the base model while updating parameters of the adapter layers—to generate an adapted model that can support the one or more original speakers of the base model in addition to the one or more additional speakers corresponding to the adapter layers.