Google llc (20240233704). RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION simplified abstract
Contents
RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION
Organization Name
Inventor(s)
Nobuyuki Morioka of Mountain View CA (US)
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 20240233704 titled 'RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION
- Simplified Explanation:**
The patent application describes a method for adapting a text-to-speech model to synthesize speech in the voice of a target speaker by using residual adapters.
- Key Features and Innovation:**
- Obtaining a text-to-speech model pre-trained on an initial data set.
- Augmenting the model with residual adapters.
- Adapting the model with a training data set to learn how to synthesize speech in the voice of a target speaker.
- Potential Applications:**
This technology can be used in personalized voice assistants, audiobook narration, voice cloning, and voice conversion applications.
- Problems Solved:**
This technology addresses the challenge of adapting text-to-speech models to mimic the voice of a specific speaker with limited training data.
- Benefits:**
- Enables personalized speech synthesis.
- Improves the quality and accuracy of synthesized speech.
- Enhances user experience in voice-based applications.
- Commercial Applications:**
"Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation" can be utilized in industries such as customer service, entertainment, education, and accessibility services to provide customized and natural-sounding speech synthesis.
- Questions about Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation:**
1. How does this technology improve the efficiency of adapting text-to-speech models for specific speakers? 2. What are the potential limitations of using residual adapters in text-to-speech adaptation?
- Frequently Updated Research:**
Researchers are continually exploring ways to enhance the adaptability and accuracy of text-to-speech models for speaker adaptation, including the optimization of residual adapters for improved performance.
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.
