mlsanityData to 14 Oct 2026

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

arXiv 2211.00585 ↗First posted 1 Nov 2022eess.AS cs.LG cs.SDcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

20 of 30 references.

References · 20

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  4. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  5. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,154
  6. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 872
  7. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
  8. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
  9. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 490
  10. 2018Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesiscited by 259
  11. 2020FastPitch: Parallel Text-to-speech with Pitch Predictioncited by 151
  12. 2018Neural Voice Cloning with a Few Samplescited by 100
8 more
  1. 2021AdaSpeech: Adaptive Text to Speech for Custom Voicecited by 98
  2. 2017Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learningcited by 67
  3. 2021TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global contextcited by 62
  4. 2021Hi-Fi Multi-Speaker English TTS Datasetcited by 60
  5. 2021One TTS Alignment To Rule Them Allcited by 47
  6. 2020BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimizationcited by 22
  7. 2020AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIANcited by 14
  8. 2022Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing modulecited by 8

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 4.

9 papers.

  1. 2023Modular Deep Learningcited by 83
  2. 2025Voice Cloning: Comprehensive Surveycited by 4
  3. 2023AdaMesh: Personalized Facial Expressions and Head Poses for Adaptive Speech-Driven 3D Facial Animationcited by 2
  4. 2023FastFit: Towards Real-Time Iterative Neural Vocoder by Replacing U-Net Encoder With Multiple STFTscited by 1
  5. 2024HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworkscited by 1
  6. 2025TTS-Transducer: End-to-End Speech Synthesis with Neural Transducercited by 1
  7. 2025UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speechcited by 1
  8. 2023Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learningcited by 0
  9. 2025Unseen Speaker and Language Adaptation for Lightweight Text-To-Speech with Adapterscited by 0