mlsanityData to 7 Oct 2026

Exploring Transfer Learning for Low Resource Emotional TTS

arXiv 1901.04276 ↗First posted 14 Jan 2019cs.SD cs.CL eess.AScited by 21

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Abstract (arXiv)

During the last few years, spoken language technologies have known a big improvement thanks to Deep Learning. However Deep Learning-based algorithms require amounts of data that are often difficult and costly to gather. Particularly, modeling the variability in speech of different speakers, different styles or different emotions with few data remains challenging. In this paper, we investigate how to leverage fine-tuning on a pre-trained Deep Learning-based TTS model to synthesize speech with a small dataset of another speaker. Then we investigate the possibility to adapt this model to have emotional TTS by fine-tuning the neutral TTS model with a small emotional dataset.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2019.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 5; 2024: 5; 2025: 1; 2026: 1.

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  1. 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 113
  2. 2022Speech Synthesis with Mixed Emotionscited by 28
  3. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  4. 2021Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speechcited by 14
  5. 2021Machine learning on small size samples: A synthetic knowledge synthesiscited by 11
  6. 2022Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentationcited by 8
  7. 2024Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotionscited by 8
  8. 2024A Review of Human Emotion Synthesis Based on Generative Technologycited by 7
  9. 2024A multilingual training strategy for low resource Text to Speechcited by 3
  10. 2022Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representationscited by 2
  11. 2022Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learningcited by 2
  12. 2023Flowchase: a Mobile Application for Pronunciation Trainingcited by 2
9 more
  1. 2025Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesiscited by 2
  2. 2022Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethicscited by 1
  3. 2023DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesiscited by 1
  4. 2023Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Datasetcited by 1
  5. 2023MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learningcited by 1
  6. 2023Expressive TTS Driven by Natural Language Prompts Using Few Human Annotationscited by 1
  7. 2024Text2Data: Low-Resource Data Generation with Textual Controlcited by 1
  8. 2024TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfercited by 0
  9. 2026LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgishcited by 0