# Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features

arXiv: https://arxiv.org/abs/1911.09645 · HTML: https://mlsanity.com/paper/1911.09645/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis](https://mlsanity.com/paper/2111.10177/) (2021)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.10168/) (2021)
- [Controllable speech synthesis by learning discrete phoneme-level prosodic representations](https://mlsanity.com/paper/2211.16307/) (2022)
- [Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning](https://mlsanity.com/paper/2406.00022/) (2024)
- [A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis](https://mlsanity.com/paper/2309.11849/) (2023)
- [Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis](https://mlsanity.com/paper/2211.01327/) (2022)
- [Adaptive Duration Model for Text Speech Alignment](https://mlsanity.com/paper/2507.22612/) (2025)
- [From Signal to Turn: Interactional Friction in Modular Speech-to-Speech Pipelines](https://mlsanity.com/paper/2512.11724/) (2025)

## Abstract (arXiv)

This paper presents a simple yet effective method to achieve prosody transfer from a reference speech signal to synthesized speech. The main idea is to incorporate well-known acoustic correlates of prosody such as pitch and loudness contours of the reference speech into a modern neural text-to-speech (TTS) synthesizer such as Tacotron2 (TC2). More specifically, a small set of acoustic features are extracted from reference audio and then used to condition a TC2 synthesizer. The trained model is evaluated using subjective listening tests and a novel objective evaluation of prosody transfer is proposed. Listening tests show that the synthesized speech is rated as highly natural and that prosody is successfully transferred from the reference speech signal to the synthesized signal.