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1. “They sure aren’t from around here”: Children’s perception of accent distance in L1 and L2 varieties of English

   https://doi.org/10.1017/S0305000924000138  

   Henry, Malachi; Bent, Tessa; Holt, Rachael F (April 2024, Journal of Child Language)

   Abstract Children exhibit preferences for familiar accents early in life. However, they frequently have more difficulty distinguishing between first language (L1) accents than second language (L2) accents in categorization tasks. Few studies have addressed children’s perception of accent strength, or the relation between accent strength and objective measures of pronunciation distance. To address these gaps, 6- and 12-year-olds and adults ranked talkers’ perceived distance from the local accent (i.e., Midland American English). Rankings were compared with objective distance measures. Acoustic and phonetic distance measures were significant predictors of ladder rankings, but there was no evidence that children and adults significantly differed in their sensitivity to accent strength. Levenshtein Distance, a phonetic distance metric, was the strongest predictor of perceptual rankings for both children and adults. As a percept, accent strength has critical implications for social judgments, which determine real world social outcomes for talkers with non-local accents. 

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2. Predicting intelligibility from pronunciation distance metrics

   Bent, T.; Holt, R.F. (August 2023, Guarant International)

   Radek Skarnitzl & Jan Volín (Ed.)

   Unfamiliar native and non-native accents can cause word recognition challenges, particularly in noisy environments, but few studies have incorporated quantitative pronunciation distance metrics to explain intelligibility differences across accents. Here, intelligibility was measured for 18 talkers -- two from each of three native, one bilingual, and five non- native accents -- in three listening conditions (quiet and two noise conditions). Two variations of the Levenshtein pronunciation distance metric, which quantifies phonemic differences from a reference accent, were assessed for their ability to predict intelligibility. An unweighted Levenshtein distance metric was the best intelligibility predictor; talker accent further predicted performance. Accuracy did not fall along a native - non-native divide. Thus, phonemic differences from the listener’s home accent primarily determine intelligibility, but other accent- specific pronunciation features, including suprasegmental characteristics, must be quantified to fully explain intelligibility across talkers and listening conditions. These results have implications for pedagogical practices and speech perception theories. 

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3. Dynamic acoustic vowel distances within and across dialects

   https://doi.org/10.1121/10.0032385  

   Clopper, Cynthia G (October 2024, The Journal of the Acoustical Society of America)

   Vowels vary in their acoustic similarity across regional dialects of American English, such that some vowels are more similar to one another in some dialects than others. Acoustic vowel distance measures typically evaluate vowel similarity at a discrete time point, resulting in distance estimates that may not fully capture vowel similarity in formant trajectory dynamics. In the current study, language and accent distance measures, which evaluate acoustic distances between talkers over time, were applied to the evaluation of vowel category similarity within talkers. These vowel category distances were then compared across dialects, and their utility in capturing predicted patterns of regional dialect variation in American English was examined. Dynamic time warping of mel-frequency cepstral coefficients was used to assess acoustic distance across the frequency spectrum and captured predicted Southern American English vowel similarity. Root-mean-square distance and generalized additive mixed models were used to assess acoustic distance for selected formant trajectories and captured predicted Southern, New England, and Northern American English vowel similarity. Generalized additive mixed models captured the most predicted variation, but, unlike the other measures, do not return a single acoustic distance value. All three measures are potentially useful for understanding variation in vowel category similarity across dialects. 

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4. Measuring the Impact of Segmental Deviation on Perceptions of Accentedness using Gradient Phonological Class Features

   https://doi.org/10.7275/scil.3102  

   Venkateswaran, Nitin; Meyer, Rachel; Wayland, Ratree (January 2025, Society for Computation in Linguistics)

   Using Phonet (Vásquez-Correa et al., 2019), a neural network-based model, we generate vector representations of speech segments consisting of phonological class probabilities and use these representations to quantify segmental deviations in the English of native Hindi speakers from American English (AE) and Indian English (IE) baselines, in order to explain how these deviations impact perceptions of accentedness by native AE speakers. The primary focus is on three AE phonemes and their realizations in Hindi English (HE) and Indian English: the labiovelar approximant /w/, often produced as the labiodental approximant [ʋ]; the alveolar stop /t/, commonly realized as the retroflex stop [ʈ]; and the rhotic approximant /ɹ/,rendered as the rhotic tap [ɾ]. Multinomial logistic regressions of Euclidean distances from HE sements to AE/IE baselines on accent ratings show that larger distances from AE baselines increase the likelihood of perceiving stronger accents while larger distances from IE baselines decrease the likelihood. Changes in the probability distributions of contrastive phonological classes are found to correlate with the strength of the perceived accent. These results offer valuable insights into the interplay between native phonology and the perception of accented speech. 

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5. The Bubble Noise Technique for Speech Perception Research

   https://doi.org/10.1044/2019_PERS-19-00058  

   Mandel, Michael I.; Grover, Vikas; Zhao, Mengxuan; Choi, Jiyoung; Shafer, Valerie L. (December 2019, Perspectives of the ASHA Special Interest Groups)

   Purpose The “bubble noise” technique has recently been introduced as a method to identify the regions in time–frequency maps (i.e., spectrograms) of speech that are especially important for listeners in speech recognition. This technique identifies regions of “importance” that are specific to the speech stimulus and the listener, thus permitting these regions to be compared across different listener groups. For example, in cross-linguistic and second-language (L2) speech perception, this method identifies differences in regions of importance in accomplishing decisions of phoneme category membership. This research note describes the application of bubble noise to the study of language learning for 3 different language pairs: Hindi English bilinguals' perception of the /v/–/w/ contrast in American English, native English speakers' perception of the tense/lax contrast for Korean fricatives and affricates, and native English speakers' perception of Mandarin lexical tone. Conclusion We demonstrate that this technique provides insight on what information in the speech signal is important for native/first-language listeners compared to nonnative/L2 listeners. Furthermore, the method can be used to examine whether L2 speech perception training is effective in bringing the listener's attention to the important cues. 

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