# Exploring Transfer Learning for Low Resource Emotional TTS

arXiv: https://arxiv.org/abs/1901.04276 · HTML: https://mlsanity.com/paper/1901.04276/

## Key facts

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

## Built on by (most cited first, 21 of 21)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Speech Synthesis with Mixed Emotions](https://mlsanity.com/paper/2208.05890/) (2022)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Machine learning on small size samples: A synthetic knowledge synthesis](https://mlsanity.com/paper/2103.01002/) (2021)
- [Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation](https://mlsanity.com/paper/2204.10020/) (2022)
- [Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions](https://mlsanity.com/paper/2409.16681/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations](https://mlsanity.com/paper/2211.06160/) (2022)
- [Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning](https://mlsanity.com/paper/2212.03558/) (2022)
- [Flowchase: a Mobile Application for Pronunciation Training](https://mlsanity.com/paper/2307.02051/) (2023)
- [Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis](https://mlsanity.com/paper/2508.13028/) (2025)
- [Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethics](https://mlsanity.com/paper/2203.09599/) (2022)
- [DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2309.12792/) (2023)
- [Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset](https://mlsanity.com/paper/2310.04982/) (2023)
- [MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learning](https://mlsanity.com/paper/2310.11541/) (2023)
- [Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations](https://mlsanity.com/paper/2311.01260/) (2023)
- [Text2Data: Low-Resource Data Generation with Textual Control](https://mlsanity.com/paper/2402.10941/) (2024)
- [TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer](https://mlsanity.com/paper/2405.02124/) (2024)
- [LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish](https://mlsanity.com/paper/2606.31947/) (2026)

## 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.