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1. 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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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)

   Abstract 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. 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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4. Characterization and normalization of second language speech intelligibility through lexical stress, speech rate, rhythm, and pauses

   https://doi.org/10.1121/10.0016224  

   Kang, Okim; Hirschi, Kevin; Hansen, John H.; Looney, Stephen (October 2022, The Journal of the Acoustical Society of America)

   While a range of measures based on speech production, language, and perception are possible (Manun et al., 2020) for the prediction and estimation of speech intelligibility, what constitutes second language (L2) intelligibility remains under-defined. Prosodic and temporal features (i.e., stress, speech rate, rhythm, and pause placement) have been shown to impact listener perception (Kang et al., 2020). Still, their relationship with highly intelligible speech is yet unclear. This study aimed to characterize L2 speech intelligibility. Acoustic analyses, including PRAAT and Python scripts, were conducted on 405 speech samples (30 s) from 102 L2 English speakers with a wide variety of backgrounds, proficiency levels, and intelligibility levels. The results indicate that highly intelligible speakers of English employ between 2 and 4 syllables per second and that higher or lower speeds are less intelligible. Silent pauses between 0.3 and 0.8 s were associated with the highest levels of intelligibility. Rhythm, measured by Δ syllable length of all content syllables, was marginally associated with intelligibility. Finally, lexical stress accuracy did not interfere substantially with intelligibility until less than 70% of the polysyllabic words were incorrect. These findings inform the fields of first and second language research as well as language education and pathology. 

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5. Mobile-Assisted Pronunciation Training With Adult Esol Learners: Background, Acceptance, Effort, and Accuracy

   https://doi.org/10.31274/psllt.13272  

   Hirschi, Kevin; Kang, Okim; Hansen, John; Cucchiarini, Catia; Strik, Helmer (September 2022, PSLLT: Proceedings of 12th Pronunciation in Second Language Learning and Teaching Conference)

   This study investigates the relationships of learner background variables of adult English for Speakers of Other Languages (ESOL) learners and a mobile App designed to promote pronunciation skills targeting features known to contribute to intelligibility. Recruited from free evening classes for English learners, 34 adult ESOL learners of mixed ESOL learning experiences, ages, lengths of residency, and first languages (L1s) completed six phoneme pair lessons on a mobile App along with a background questionnaire and technology acceptance survey (Venkatesh et al., 2012). A series of Linear Mixed-Effect Model (LMEM) analyses were performed on learner background variables, technology acceptance, learner effort, and accuracy. The results found a minimal relationship between age, technology acceptance, and effort (7.68%) but a moderate to large relationship between age, technology acceptance and accuracy of consonants (39.70%) and vowels (64.26%). The implications are that learner use of mobile devices for L2 pronunciation training is moderated by various learner-related factors and the findings offer supportive evidence for designing mobile-based applications for a wide variety of backgrounds. 

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