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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. 

This paper reports on the creation and composition of a new corpus of children’s speech, the Ohio Child Speech Corpus, which is publicly available on the Talkbank-CHILDES website. The audio corpus contains speech samples from 303 children ranging in age from 4 – 9 years old, all of whom participated in a seven-task elicitation protocol conducted in a science museum lab. In addition, an interactive social robot controlled by the researchers joined the sessions for approximately 60% of the children, and the corpus itself was collected in the peri‑pandemic period. Two analyses are reported that highlighted these last two features. One set of analyses found that the children spoke significantly more in the presence of the robot relative to its absence, but no effects of speech complexity (as measured by MLU) were found for the robot’s presence. Another set of analyses compared children tested immediately post-pandemic to children tested a year later on two school-readiness tasks, an Alphabet task and a Reading Passages task. This analysis showed no negative impact on these tasks for our highly-educated sample of children just coming off of the pandemic relative to those tested later. These analyses demonstrate just two possible types of questions that this corpus could be used to investigate. 

Online data collection allows for access to diverse populations. In the current study, we used online recruitment and data collection methods to obtain a corpus of read speech from adult talkers representing three authentic regional dialects of American English and one novel dialect created for the corpus. The authentic dialects (New England, Northern, and Southern American English) are each represented by 8–10 talkers, ranging in age from 22 to 75 years old. The novel dialect was produced by five Spanish-English bilinguals with training in linguistics, who were asked to produce Spanish /o/ in an otherwise English segmental context. One vowel contrast was selected for each dialect, in which the vowels within the contrast are acoustically more similar in the target dialect than in the other dialects. Each talker produced one familiar short story with 40 tokens of each vowel within the target contrast for their dialect, as well as a set of real words and nonwords that represent both the target vowel contrast for their dialect and the other three vowel contrasts for comparison across dialects. Preliminary acoustic analysis reveals both cross-dialect and within-dialect variability in the target vowel contrasts. The corpus materials are available to the scholarly community. 

Listeners attend to variation in segmental and prosodic cues when judging accent strength. The relative contributions of these cues to perceptions of accentedness in English remains open for investigation, although objective accent distance measures (such as Levenshtein distance) appear to be reliable tools for predicting perceptual distance. Levenshtein distance, however, only accounts for phonemic information in the signal. The purpose of the current study was to examine the relative contributions of phonemic (Levenshtein) and holistic acoustic (dynamic time warping) distances from the local accent to listeners’ accent rankings for nine non-local native and nonnative accents. Listeners (n =52) ranked talkers on perceived distance from the local accent (Midland American English) using a ladder task for three sentence-length stimuli. Phonemic and holistic acoustic distances between Midland American English and the other accents were quantified using both weighted and unweighted Levenshtein distance measures, and dynamic time warping (DTW). Results reveal that all three metrics contribute to perceived accent distance, with the weighted Levenshtein slightly outperforming the other measures. Moreover, the relative contribution of phonemic and holistic acoustic cues was driven by the speaker’s accent. Both nonnative and non-local native accents were included in this study, and the benefits of considering both of these accent groups in studying phonemic and acoustic cues used by listeners is discussed. 

This corpus was collected in the Language Sciences Research Lab, a working lab embedded inside of a science museum: the Center of Science and Industry in Columbus, Ohio, USA. Participants were recruited from the floor of the museum and run in a semi-public space. Three distinctive features of the corpus are: (1) an interactive social robot (specifically, a Jibo robot) was present and participated in the sessions for roughly half the children; (2) all children were recorded with a lapel mic generating high quality audio (available through CHILDES), as well as a distal table mic generating low quality audio (available on request) to facilitate strong tests of automated speech processing on the data; and (3) the data were collected in the peri-pandemic period, beginning in the summer of 2021 just after COVID-19 restrictions were being eased and ending in the summer of 2022 – thus providing a snapshot of language development in a distinctive time of the world. A YouTube video on the Jibo robot is available here . 

Abstract Online testing for behavioral research has become an increasingly used tool. Although more researchers have been using online data collection methods, few studies have assessed the replicability of findings for speech intelligibility tasks. Here we assess intelligibility in quiet and two noise-added conditions for several different accents of English (Midland American, Standard Southern British, Scottish, German-accented, Mandarin-accented, Japanese-accented, and Hindi-English bilingual). Participants were tested in person at a museum-based laboratory and online. Results showed little to no difference between the two settings for the easier noise condition and in quiet, but large performance differences in the most difficult noise condition with an advantage for the participants tested online. Technology-based variables did not appear to drive the setting effect, but experimenter presence may have influenced response strategy for the in-person group and differences in demographics could have provided advantages for the online group. Additional research should continue to investigate how setting, demographic factors, experimenter presence, and motivational factors interact to determine performance in speech perception experiments.