# Intelligibility of Text-to-Speech Systems for Mathematical Expressions

arXiv: https://arxiv.org/abs/2506.11086 · HTML: https://mlsanity.com/paper/2506.11086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [Recent Advances in Speech Language Models: A Survey](https://mlsanity.com/paper/2410.03751/) (2024)
- [MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into LaTeX Formulas for Improved Readability](https://mlsanity.com/paper/2408.07081/) (2024)
- [TeXBLEU: Automatic Metric for Evaluate LaTeX Format](https://mlsanity.com/paper/2409.06639/) (2024)
- [MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula](https://mlsanity.com/paper/2412.15655/) (2024)
- [MathReader : Text-to-Speech for Mathematical Documents](https://mlsanity.com/paper/2501.07088/) (2025)

## Built on by (most cited first, 3 of 3)

- [Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations](https://mlsanity.com/paper/2602.01030/) (2026)
- [Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences](https://mlsanity.com/paper/2508.03542/) (2025)
- [Measuring Visual Understanding in Telecom domain: Performance Metrics for Image-to-UML conversion using VLMs](https://mlsanity.com/paper/2509.11667/) (2025)

## Abstract (arXiv)

There has been limited evaluation of advanced Text-to-Speech (TTS) models with Mathematical eXpressions (MX) as inputs. In this work, we design experiments to evaluate quality and intelligibility of five TTS models through listening and transcribing tests for various categories of MX. We use two Large Language Models (LLMs) to generate English pronunciation from LaTeX MX as TTS models cannot process LaTeX directly. We use Mean Opinion Score from user ratings and quantify intelligibility through transcription correctness using three metrics. We also compare listener preference of TTS outputs with respect to human expert rendition of same MX. Results establish that output of TTS models for MX is not necessarily intelligible, the gap in intelligibility varies across TTS models and MX category. For most categories, performance of TTS models is significantly worse than that of expert rendition. The effect of choice of LLM is limited. This establishes the need to improve TTS models for MX.