18493770. RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION simplified abstract (GOOGLE LLC)

RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION

Organization Name

GOOGLE LLC

Inventor(s)

Nobuyuki Morioka of Mountain View CA (US)

Byungha Chun of Tokyo (JP)

Nanxin Chen of Mountain View CA (US)

Yu Zhang of Mountain View CA (US)

Yifan Ding of Mountain View CA (US)

RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18493770 titled 'RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION

Simplified Explanation

The abstract describes a method for residual adapters for few-shot text-to-speech speaker adaptation, where a text-to-speech (TTS) model is augmented with residual adapters to learn how to synthesize speech in the voice of a target speaker using an adaptation training data set.

• The method involves obtaining a TTS model pre-trained on an initial data set and augmenting it with residual adapters.
• An adaptation training data set with spoken utterances from a target speaker is used to adapt the TTS model to synthesize speech in the voice of the target speaker.
• The adaptation process optimizes the stack of residual adapters while keeping the parameters of the TTS model frozen.

Potential Applications

This technology could be applied in personalized voice assistants, voice cloning for entertainment purposes, and voice banking for individuals with speech impairments.

Problems Solved

This technology solves the problem of quickly adapting a TTS model to synthesize speech in the voice of a specific target speaker with limited training data.

Benefits

The benefits of this technology include improved personalization in text-to-speech systems, enhanced user experience in voice applications, and increased accessibility for individuals with speech disabilities.

Potential Commercial Applications

Commercial applications of this technology include voice conversion services, customized voice interfaces for products, and voice avatars for virtual assistants.

Possible Prior Art

One possible prior art in this field is the use of transfer learning techniques in text-to-speech systems to adapt models to new speakers with limited data.

How does this method compare to existing speaker adaptation techniques in text-to-speech systems?

This article does not provide a direct comparison with other speaker adaptation methods in text-to-speech systems. It would be interesting to know the performance metrics and efficiency of this method compared to traditional adaptation techniques.

What are the potential limitations or challenges of using residual adapters for speaker adaptation in text-to-speech systems?

The article does not address any potential limitations or challenges that may arise when using residual adapters for speaker adaptation. It would be valuable to understand any constraints or drawbacks associated with this approach.

Original Abstract Submitted

A method for residual adapters for few-shot text-to-speech speaker adaptation includes obtaining a text-to-speech (TTS) model configured to convert text into representations of synthetic speech, the TTS model pre-trained on an initial training data set. The method further includes augmenting the TTS model with a stack of residual adapters. The method includes receiving an adaption training data set including one or more spoken utterances spoken by a target speaker, each spoken utterance in the adaptation training data set paired with corresponding input text associated with a transcription of the spoken utterance. The method also includes adapting, using the adaption training data set, the TTS model augmented with the stack of residual adapters to learn how to synthesize speech in a voice of the target speaker by optimizing the stack of residual adapters while parameters of the TTS model are frozen.