Targeted adaptation in infants following live exposure to an accented talker

Paquette‐Smith, Melissa; Cooper, Angela; Johnson, Elizabeth

doi:10.1017/s030500092000029x

Cited by 9 publications

(9 citation statements)

References 47 publications

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“…Replicating both BB08 and other studies across a variety of paradigms, we find clear support that exposure to a few dozen sentences of L2-accented speech facilitates subsequent comprehension of speech from the same talker (talker-specific adaptation to an L2 talker, e.g., Clarke & Garrett, 2004;Gordon-Salant et al, 2010;Paquette-Smith et al, 2021;Weil, 2001; for review, see Baese-Berk, 2018). This result received strong support across all four test talkers in both Experiment 1a and 1b.…”

Section: Cross-talker Generalization In Speech Perception E35supporting

confidence: 84%

“…Bradlow and Bent interpreted these results as evidence that listeners can learn talker-independent representations about an L2 accent, provided they are exposed to sufficient variability (as in the multitalker condition). Both in its categorical form (multitalker exposure as a requirement for generalization) and in more gradient interpretations (multitalker exposure as an additional benefit beyond single-talker exposure), this hypothesis has been influential within research on speech perception (e.g., Baese-Berk, 2018; Kleinschmidt & Jaeger, 2015) and beyond (e.g., Paquette-Smith et al, 2021; Potter & Saffran, 2017; Schmale et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

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Cross-talker generalization in the perception of nonnative speech: A large-scale replication.

Xie¹,

Liu²

2021

Journal of Experimental Psychology: General

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Speech perception depends on the ability to generalize previously experienced input effectively across talkers. How such cross-talker generalization is achieved has remained an open question. In a seminal study, Bradlow & Bent (2008, henceforth BB08) found that exposure to just 5 min of accented speech can elicit improved recognition that generalizes to an unfamiliar talker of the same accent (N = 70 participants). Cross-talker generalization was, however, only observed after exposure to multiple talkers of the accent, not after exposure to a single accented talker. This contrast between single-and multitalker exposure has been highly influential beyond research on speech perception, suggesting a critical role of exposure variability in learning and generalization. We assess the replicability of BB08's findings in two large-scale perception experiments (total N = 640) including 20 unique combinations of exposure and test talkers. Like BB08, we find robust evidence for cross-talker generalization after multitalker exposure. Unlike BB08, we also find evidence for generalization after single-talker exposure. The degree of cross-talker generalization depends on the specific combination of exposure and test talker. This and other recent findings suggest that exposure to cross-talker variability is not necessary for cross-talker generalization. Variability during exposure might affect generalization only indirectly, mediated through the informativeness of exposure about subsequent speech during test: Similarity-based inferences can explain both the original BB08 and the present findings. We present Bayesian data analysis, including Bayesian meta-analyses and replication tests for generalized linear mixed models. All data, stimuli, and reproducible literate (R markdown) code are shared via OSF.

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Section: Cross-talker Generalization In Speech Perception E35supporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Cross-talker generalization in the perception of nonnative speech: A large-scale replication.

Xie¹,

Liu²

2021

Journal of Experimental Psychology: General

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“…Using CALL system to realize automatic correction of spoken recording will reduce the workload of teachers [ 5 ]. In recent years, with the improvement of computer speed and speech recognition technology, the use of automatic speech recognition (ASR) technology to evaluate spoken speech has become a research hotspot [ 6 ]. At present, the CALL system has successfully realized the automatic correction of oral reading questions.…”

Section: Introductionmentioning

confidence: 99%

Research on Open Oral English Scoring System Based on Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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This study designs and implements a scoring system for open-spoken English using NN technology. The system scores the oral recording from the phonetic level and the text level, respectively, and can comprehensively evaluate its oral level. The system will separately score the spoken speech and the spoken content through different scoring models and add the scoring results as the final score, in which the spoken content is obtained by text transcription of the recording by an external speech recognition engine. An acoustic sensor is adopted to collect pronunciation signals of spoken English. Modern signal processing and automatic pattern recognition technology are used to distinguish the quality of spoken pronunciation. Similar semantic units are marked between acoustic feature sequences, which make use of the parallel algorithm processing mode of multi-computing cores of modern GPU and allow multiple units to independently execute the comparison algorithm at the same time. Experiments show that the model in this study achieves better comprehensive scoring performance. The scoring model is of great significance to the development of educational informatization and intelligence, and it also provides a reference for the construction of intelligent oral scoring system.

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“…One possibility is that the French accent was simply more difficult to understand compared with the other accents. We know by their second birthday children can adapt to unfamiliar regional (van Heugten et al, 2015) and non-native accents (Paquette-Smith et al, 2020; van Heugten et al, 2018). However, just because children are able to adapt, does not mean that processing the French accent was not effortful.…”

Section: Discussionmentioning

confidence: 99%

How sociolinguistic factors shape children’s subjective impressions of teacher quality

Paquette‐Smith

Buckler

Johnson

2022

Quarterly Journal of Experimental Psychology

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When university students are asked to rate their instructors, their evaluations are often influenced by the demographic characteristics of the instructor – such as the instructor’s race, gender or language background. These influences can manifest in unfair systematic biases against particular groups of teachers, and hamper movements to promote diversity in higher education. When and how do these biases develop? Here, we begin to address these questions by examining children’s sociolinguistic biases against teachers who speak with different accents. To do this, we presented five-year-old Canadian English-speaking children with pairs of adult talkers. Children were asked to select ‘who they’d like to be their teacher’ then they rated ‘how good of a teacher’ they thought each talker would be on a 5-point scale. In each trial, one talker spoke in the locally dominant variety of Canadian English, and the other spoke in a different accent. Children strongly preferred Canadian-accented teachers over teachers who spoke with non-native (i.e., French or Dutch) accents, but also demonstrated a preference for Canadian teachers over teachers who spoke with non-local regional accents (i.e., Australian or British). In line with the binary choice data, children rated the Canadian talkers more favorably. The relationship between the gender of the teacher and the gender of the child also impacted ratings. This work demonstrates that even at the onset of formal education, children may already exhibit signs of accent-based biases. We discuss these findings in relation to the growing literature on implicit bias in higher education.

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Targeted adaptation in infants following live exposure to an accented talker

Cited by 9 publications

References 47 publications

Cross-talker generalization in the perception of nonnative speech: A large-scale replication.

Cross-talker generalization in the perception of nonnative speech: A large-scale replication.

Research on Open Oral English Scoring System Based on Neural Network

How sociolinguistic factors shape children’s subjective impressions of teacher quality

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