How pronunciation distance impacts word recognition in children and adults

Abstract

Although unfamiliar accents can pose word identification challenges for children and adults, few studies have directly compared perception of multiple nonnative and regional accents or quantified how the extent of deviation from the ambient accent impacts word identification accuracy across development. To address these gaps, 5- to 7-year-old children's and adults' word identification accuracy with native (Midland American, British, Scottish), nonnative (German-, Mandarin-, Japanese-accented English) and bilingual (Hindi-English) varieties (one talker per accent) was tested in quiet and noise. Talkers' pronunciation distance from the ambient dialect was quantified at the phoneme level using a Levenshtein algorithm adaptation. Whereas performance was worse on all non-ambient dialects than the ambient one, there were only interactions between talker and age (child vs adult or across age for the children) for a subset of talkers, which did not fall along the native/nonnative divide. Levenshtein distances significantly predicted word recognition accuracy for adults and children in both listening environments with similar impacts in quiet. In noise, children had more difficulty overcoming pronunciations that substantially deviated from ambient dialect norms than adults. Future work should continue investigating how pronunciation distance impacts word recognition accuracy by incorporating distance metrics at other levels of analysis (e.g., phonetic, suprasegmental).

Comparing alignment toward American, British, and Indian English text-to-speech (TTS) voices: influence of social attitudes and talker guise

“…While tentative, another possible explanation for greater alignment toward Indian English is phonetic distance. Recent work has shown a greater number of phonetic "edits" from American English varieties to Hindi-English than to RP (Bent et al, 2021). In our stimuli, many items contained a contrast in VOT in addition to a contrast in vowel length or quality, meaning that there was more space for US speakers to converge toward IN voices than RP voices.…”

“…Both RP and IN English differ from US English in vowel quality and vowel length, and IN English additionally differs from US English in voice-onset time (VOT) of word-initial stops (Awan and Stine, 2011). The target words selected for this study were chosen to emphasize the phonetic distance between US to another US speaker (no change), US to RP (small change), and US to IN English (larger change) (Bent et al, 2021). Relative to US English, the stimuli differ in either vowel length or quality in RP and/or IN English, and in VOT in IN English (Wells, 1982;Schmitt, 2007;Awan and Stine, 2011).…”

Navigating accent bias in German: children's social preferences for a second-language accent over a first-language regional accent

“…used Levenshtein distance to measure such mismatches, revealing that children may face greater difficulties identifying words in an unfamiliar L1 regional accent than a L2 accent. These effects of pronunciation distance have been replicated and extended to other L1 and L2 varieties (Bent et al, 2021;Hanulíková, 2021), challenging the widespread view that L1 speech is generally more intelligible, cognitively less effortful, and socially more preferred than L2 speech. Moreover, Weatherhead et al (2019) underscore the role of prosodic patterns that can affect preference judgments.…”

“…An alternative explanation pertains to the acoustic and perceptual cues of the accents themselves. Several studies have demonstrated that factors such as familiarity, listening effort, and pronunciation distance can influence how individuals, including children, respond to L2 accents and regional varieties (Van Engen and Peelle, 2014;Bent, 2018;Hanulíková, 2019Hanulíková, , 2021Weatherhead et al, 2019;Bent et al, 2021). Listening effort is associated with varying processing load for different degrees of acoustic mismatch between speech produced by a speaker and a listener's variety (Van Engen and Peelle, 2014).…”

The inter-relationship between word learning, native phonology and production practice through cross-situational statistics

“…Given that the participants were we trained on the form and meaning of distinct words, perhaps they were approximating the word shape and producing it as such, according to it being phonologically closer to the target than to the other words. Further research could code the participant's accuracy in word productions according to the level at which they conform to the target, relative to the other target items using an Levenshtein algorithm adaptation (e.g., Bent et al, 2021;Levenshtein, 1966).…”