# Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis

arXiv: https://arxiv.org/abs/1903.11570 · HTML: https://mlsanity.com/paper/1903.11570/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Analysis and Assessment of Controllability of an Expressive Deep Learning-based TTS system](https://mlsanity.com/paper/2103.04097/) (2021)
- [Flowchase: a Mobile Application for Pronunciation Training](https://mlsanity.com/paper/2307.02051/) (2023)
- [DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2309.12792/) (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)
- [TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer](https://mlsanity.com/paper/2405.02124/) (2024)

## Abstract (arXiv)

The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.