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2. Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers
 
 # Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 Fine-tuning is a popular method for adapting text-to-speech (TTS) models to new speakers. However this approach has some challenges. Usually fine-tuning requires several hours of high quality speech per speaker. There is also that fine-tuning will negatively affect the quality of speech synthesis for previously learnt speakers. In this paper we propose an alternative approach for TTS adaptation based on using parameter-efficient adapter modules. In the proposed approach, a few small adapter modules are added to the original network. The original weights are frozen, and only the adapters are fine-tuned on speech for new speaker. The parameter-efficient fine-tuning approach will produce a new model with high level of parameter sharing with original model. Our experiments on LibriTTS, HiFi-TTS and VCTK datasets validate the effectiveness of adapter-based method through objective and subjective metrics.



 ## Authors



Cheng-Ping Hsieh (University of California San Diego)

Subhankar Ghosh (NVIDIA)

Boris Ginsburg (NVIDIA)

 

 

 ## Publication Date



Tuesday, November 1, 2022

 

 ## Research Area



[Speech Processing](/research-area/speech-processing)

 

 

 ## External Links



[Paper](https://arxiv.org/abs/2211.00585)