# Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors

arXiv: https://arxiv.org/abs/2107.08337 · HTML: https://mlsanity.com/paper/2107.08337/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Data-Driven Investigation of Noise-Adaptive Utterance Generation with Linguistic Modification](https://mlsanity.com/paper/2210.10252/) (2022)
- [Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?](https://mlsanity.com/paper/2408.04029/) (2024)

## Abstract (arXiv)

Listening in noisy environments can be difficult even for individuals with a normal hearing thresholds. The speech signal can be masked by noise, which may lead to word misperceptions on the side of the listener, and overall difficulty to understand the message. To mitigate hearing difficulties on listeners, a co-operative speaker utilizes voice modulation strategies like Lombard speech to generate noise-robust utterances, and similar solutions have been developed for speech synthesis systems. In this work, we propose an alternate solution of choosing noise-robust lexical paraphrases to represent an intended meaning. Our results show that lexical paraphrases differ in their intelligibility in noise. We evaluate the intelligibility of synonyms in context and find that choosing a lexical unit that is less risky to be misheard than its synonym introduced an average gain in comprehension of 37% at SNR -5 dB and 21% at SNR 0 dB for babble noise.