Covertly improving intelligibility with data-driven adaptations of speech timing
2026-03-31 • Computation and Language
Computation and LanguageSound
AI summaryⓘ
The authors studied how changing the speed of certain parts of speech can help people understand words better, especially when the speech is hard to hear or in another language. They found that slowing down specific sounds near tricky vowels helps both native and non-native listeners understand speech more clearly, even if listeners don't notice the improvement. They also built a computer system that mimics this helpful speech pattern. Interestingly, people thought that slowing down the whole sentence was clearer, but it actually made understanding worse. This research suggests smarter ways to adjust speech speed to improve listening, especially with machine-generated speech.
speech rateintelligibilityvowel contrastreverse-correlationL1 and L2 listenerstext-to-speechacoustic contextspeech comprehensionmachine-generated speechtemporal structure
Authors
Paige Tuttösí, Angelica Lim, H. Henny Yeung, Yue Wang, Jean-Julien Aucouturier
Abstract
Human talkers often address listeners with language-comprehension challenges, such as hard-of-hearing or non-native adults, by globally slowing down their speech. However, it remains unclear whether this strategy actually makes speech more intelligible. Here, we take advantage of recent advancements in machine-generated speech allowing more precise control of speech rate in order to systematically examine how targeted speech-rate adjustments may improve comprehension. We first use reverse-correlation experiments to show that the temporal influence of speech rate prior to a target vowel contrast (ex. the tense-lax distinction) in fact manifests in a scissor-like pattern, with opposite effects in early versus late context windows; this pattern is remarkably stable both within individuals and across native L1-English listeners and L2-English listeners with French, Mandarin, and Japanese L1s. Second, we show that this speech rate structure not only facilitates L2 listeners' comprehension of the target vowel contrast, but that native listeners also rely on this pattern in challenging acoustic conditions. Finally, we build a data-driven text-to-speech algorithm that replicates this temporal structure on novel speech sequences. Across a variety of sentences and vowel contrasts, listeners remained unaware that such targeted slowing improved word comprehension. Strikingly, participants instead judged the common strategy of global slowing as clearer, even though it actually increased comprehension errors. Together, these results show that targeted adjustments to speech rate significantly aid intelligibility under challenging conditions, while often going unnoticed. More generally, this paper provides a data-driven methodology to improve the accessibility of machine-generated speech which can be extended to other aspects of speech comprehension and a wide variety of listeners and environments.