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

TL;DR

This paper investigates using noise-robust lexical paraphrases to improve speech comprehension in noisy environments, showing significant gains in intelligibility at low signal-to-noise ratios.

Contribution

It introduces the novel idea of selecting lexical paraphrases based on noise robustness to enhance understanding in noisy conditions.

Findings

Lexical paraphrases vary in noise intelligibility.

Choosing less risky synonyms improves comprehension by up to 37%.

Significant gains observed at low SNR levels.

Abstract

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.