Google llc (20240135915). RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION simplified abstract
Contents
- 1 RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
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 20240135915 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 model is augmented with residual adapters to adapt to a target speaker using a small amount of adaptation training data.
- Obtaining a text-to-speech (TTS) model pre-trained on an initial training data set.
- Augmenting the TTS model with a stack of residual adapters.
- Receiving an adaptation training data set with spoken utterances from a target speaker paired with corresponding input text.
- Adapting the TTS model with the residual adapters using the adaptation training data set to synthesize speech in the voice of the target speaker.
Potential Applications
This technology could be applied in personalized virtual assistants, audiobook narration, language learning applications, and voice cloning services.
Problems Solved
This technology solves the problem of adapting a text-to-speech model to a specific speaker with limited training data, enabling more natural and accurate speech synthesis for individual voices.
Benefits
The benefits of this technology include improved speech synthesis quality for specific speakers, reduced data requirements for speaker adaptation, and enhanced user experience in applications requiring personalized speech output.
Potential Commercial Applications
Potential commercial applications of this technology include voice banking services, personalized voice assistants for smart devices, custom voice message creation services, and voice conversion software for entertainment and media industries.
Possible Prior Art
One possible prior art in this field is the use of speaker adaptation techniques in speech recognition systems to improve accuracy and performance for individual speakers. Another prior art could be the use of residual adapters in image processing for enhancing image quality and detail.
Unanswered Questions
How does this method compare to other speaker adaptation techniques in terms of performance and efficiency?
The article does not provide a direct comparison with other speaker adaptation methods, leaving the reader to wonder about the relative advantages of this approach.
What are the limitations of using residual adapters for speaker adaptation, and how can they be addressed?
The article does not discuss any potential limitations or challenges associated with using residual adapters for speaker adaptation, leaving room for further exploration of this topic.
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.
