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1. Cloud-based Platform for Indigenous Language Sound Education

   Chen, Min ; Lee, Chris ; Fish, Naatosi ; Miyashita, Mizuki ; Randall, James ( March 2024 , Association for Computational Linguistics)

   Moeller, Sarah ; Agyapong, Godfred ; Arppe, Antti ; Chaudhary, Aditi ; Rijhwani, Shruti ; Cox, Christopher ; Henke, Ryan ; Palmer, Alexis ; Rosenblum, Daisy ; Schwartz, Lane (Ed.)

   Blackfoot is challenging for English speaking instructors and learners to acquire because it exhibits unique pitch patterns. This study presents MeTILDA (Melodic Transcription in Language Documentation and Application) as a solution to teaching pitch patterns distinct from English. Specifically, we explore ways to improve data visualization through a visualized pronunciation teaching guide called Pitch Art. The working materials can be downloaded or stored in the cloud for further use and collaboration. These features are aimed to facilitate teachers in developing curriculum for learning pronunciation, and provide students with an interactive and integrative learning environment to better understand Blackfoot language and pronunciation. 

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2. Data‐Driven Learning for Pronunciation: Perception and Production of Lexical Stress and Prominence in Academic English

   https://doi.org/10.1002/tesq.3302  

   Hirschi, Kevin ; Kang, Okim ( January 2024 , TESOL Quarterly)

   Issues of intelligibility may arise amongst English learners when acquiring new words and phrases in North American academic settings, perhaps in part due to limited linguistic data available to the learner for understanding language use patterns. To this end, this paper examines the effects of Data‐Driven Learning for Pronunciation (DDLfP) on lexical stress and prominence in the US academic context. 65 L2 English learners in North American universities completed a diagnostic and pretest with listening and speaking items before completing four online lessons and a posttest on academic words and formulas (i.e., multi‐word sequences). Experimental group participants (n = 40) practiced using an audio corpus of highly proficient L2 speakers while comparison group participants (n = 25) were given teacher‐created pronunciation materials. Logistic regression results indicated that the group who used the corpus significantly increased their recognition of prominence in academic formulas. In the spoken tasks, both groups improved in their lexical stress pronunciation, but only the DDLfP learners improved their production of prominence in academic formulas. Learners reported that they valued DDLfP efforts for pronunciation learning across contexts and speakers. Findings have implications for teachers of L2 pronunciation and support the use of corpora for language teaching and learning.

    

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3. Assessment of Non-Native Speech Intelligibility using Wav2vec2-based Mispronunciation Detection and Multi-level Goodness of Pronunciation Transformer

   https://doi.org/10.21437/Interspeech.2023-2371  

   Shekar, Ram C. ; Yang, Mu ; Hirschi, Kevin ; Looney, Stephen ; Kang, Okim ; Hansen, John H. ( August 2023 , ISCA INTERSPEECH-2023)

   N/A (Ed.)

   Automatic pronunciation assessment (APA) plays an important role in providing feedback for self-directed language learners in computer-assisted pronunciation training (CAPT). Several mispronunciation detection and diagnosis (MDD) systems have achieved promising performance based on end-to-end phoneme recognition. However, assessing the intelligibility of second language (L2) remains a challenging problem. One issue is the lack of large-scale labeled speech data from non-native speakers. Additionally, relying only on one aspect (e.g., accuracy) at a phonetic level may not provide a sufficient assessment of pronunciation quality and L2 intelligibility. It is possible to leverage segmental/phonetic-level features such as goodness of pronunciation (GOP), however, feature granularity may cause a discrepancy in prosodic-level (suprasegmental) pronunciation assessment. In this study, Wav2vec 2.0-based MDD and Goodness Of Pronunciation feature-based Transformer are employed to characterize L2 intelligibility. Here, an L2 speech dataset, with human-annotated prosodic (suprasegmental) labels, is used for multi-granular and multi-aspect pronunciation assessment and identification of factors important for intelligibility in L2 English speech. The study provides a transformative comparative assessment of automated pronunciation scores versus the relationship between suprasegmental features and listener perceptions, which taken collectively can help support the development of instantaneous assessment tools and solutions for L2 learners. 

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4. Determinants of voice recognition in monolingual and bilingual listeners

   https://doi.org/10.1017/S1366728919000075  

   Theodore, Rachel M. ; Flanagan, Erin G. ( January 2020 , Bilingualism: Language and Cognition)

   Abstract Recent findings demonstrate a bilingual advantage for voice processing in children, but the mechanism supporting this advantage is unknown. Here we examined whether a bilingual advantage for voice processing is observed in adults and, if so, if it reflects enhanced pitch perception or inhibitory control. Voice processing was assessed for monolingual and bilingual adults using an associative learning identification task and a discrimination task in English (a familiar language) and French (an unfamiliar language). Participants also completed pitch perception, flanker, and auditory Stroop tasks. Voice processing was improved for the familiar compared to the unfamiliar language and reflected individual differences in pitch perception (both tasks) and inhibitory control (identification task). However, no bilingual advantage was observed for either voice task, suggesting that the bilingual advantage for voice processing becomes attenuated during maturation, with performance in adulthood reflecting knowledge of linguistic structure in addition to general auditory and inhibitory control abilities. 

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5. Spatial Metaphor and the Development of Cross-Domain Mappings in Early Childhood

   Starr, Ariel ; Srinivasan, Mahesh ( April 2018 , Developmental psychology)

   Spatial language is often used metaphorically to describe other domains, including time (long sound) and pitch (high sound). How does experience with these metaphors shape the ability to associate space with other domains? Here, we tested 3- to 6-year-old English-speaking children and adults with a cross-domain matching task. We probed cross-domain relations that are expressed in English metaphors for time and pitch (length-time and height-pitch), as well as relations that are unconventional in English but expressed in other languages (size-time and thickness-pitch). Participants were tested with a perceptual matching task, in which they matched between spatial stimuli and sounds of different durations or pitches, and a linguistic matching task, in which they matched between a label denoting a spatial attribute, duration, or pitch, and a picture or sound representing another dimension. Contrary to previous claims that experience with linguistic metaphors is necessary for children to make cross-domain mappings, children performed above chance for both familiar and unfamiliar relations in both tasks, as did adults. Children’s performance was also better when a label was provided for one of the dimensions, but only when making length-time, size-time, and height-pitch mappings (not thickness-pitch mappings). These findings suggest that, although experience with metaphorical language is not necessary to make cross-domain mappings, labels can promote these mappings, both when they have familiar metaphorical uses (e.g., English ‘long’ denotes both length and duration), and when they describe dimensions that share a common ordinal reference frame (e.g., size and duration, but not thickness and pitch). 

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