# Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion

arXiv: https://arxiv.org/abs/2008.05809 · HTML: https://mlsanity.com/paper/2008.05809/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [UmbraTTS: Adapting Text-to-Speech to Environmental Contexts with Flow Matching](https://mlsanity.com/paper/2506.09874/) (2025)
- [Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning](https://mlsanity.com/paper/2507.09310/) (2025)
- [Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS](https://mlsanity.com/paper/2606.23176/) (2026)
- [Zero-Shot Lombard Speech Synthesis with Controllable Style Embeddings](https://mlsanity.com/paper/2601.12966/) (2026)

## Abstract (arXiv)

The increased adoption of digital assistants makes text-to-speech (TTS) synthesis systems an indispensable feature of modern mobile devices. It is hence desirable to build a system capable of generating highly intelligible speech in the presence of noise. Past studies have investigated style conversion in TTS synthesis, yet degraded synthesized quality often leads to worse intelligibility. To overcome such limitations, we proposed a novel transfer learning approach using Tacotron and WaveRNN based TTS synthesis. The proposed speech system exploits two modification strategies: (a) Lombard speaking style data and (b) Spectral Shaping and Dynamic Range Compression (SSDRC) which has been shown to provide high intelligibility gains by redistributing the signal energy on the time-frequency domain. We refer to this extension as Lombard-SSDRC TTS system. Intelligibility enhancement as quantified by the Intelligibility in Bits (SIIB-Gauss) measure shows that the proposed Lombard-SSDRC TTS system shows significant relative improvement between 110% and 130% in speech-shaped noise (SSN), and 47% to 140% in competing-speaker noise (CSN) against the state-of-the-art TTS approach. Additional subjective evaluation shows that Lombard-SSDRC TTS successfully increases the speech intelligibility with relative improvement of 455% for SSN and 104% for CSN in median keyword correction rate compared to the baseline TTS method.