Expanding the scope of L2 intelligibility research

Abstract

<jats:title>Abstract</jats:title> <jats:p>This study investigated relationships among intelligibility, comprehensibility, and accentedness in the speech of L2 learners of Spanish who completed a prompted response speaking task. Thirty native Spanish listeners from Spain were recruited through Amazon Mechanical Turk to transcribe and rate extracted utterances, which were also coded for grammatical and phonemic errors, and speaking rate. Descriptively, although most utterances were intelligible, their comprehensibility and accentedness varied substantially. Mixed-effects modeling showed that comprehensibility was significantly associated with intelligibility whereas accentedness was not. Additionally, phonemic and grammatical errors were significant predictors of intelligibility and comprehensibility, but only phonemic errors were significantly related to accentedness. Overall, phonemic errors displayed a stronger negative association with the listener-based dimensions than grammatical errors. These findings suggest that English-speaking learners of Spanish are not as uniformly intelligible and comprehensible as FL instructors might believe and shed light on relationships among speech constructs in an L2 other than English.</jats:p>

Doing L2 Speech Research Online: Why and How to Collect Online Ratings Data

“…As for rater fit statistics, a range of 0.50-1.50 for infit values can be interpreted as good internal consistency (Eckes, 2015), and all but one listener (infit = 2.08) was in that range. With respect to the rating categories, foreign accent was the most severely rated (logit value of 0.78), which is in line with previous research (e.g., Munro & Derwing, 1995;Nagle & Huensch, 2020), while comprehensibility and fluency yielded more lenient ratings (logit values of -0.57 and -0.21, respectively). According to Eckes (2015), rating scale effectiveness may also be examined through fit statistics such as the mean-square outfit statistic, which should not exceed 2.…”

“…These three constructs, while interrelated, capture distinct facets of oral communicative competence. For one, accented speech is often highly comprehensible (Munro & Derwing, 1995;Nagle & Huensch, 2020), and numerous studies have shown that different bundles of linguistic features predict comprehensibility and accentedness. For instance, Trofimovich and Isaacs (2012) found that rhythm (vowel reduction ratio) was the strongest predictor of accent, whereas type frequency was the strongest predictor of comprehensibility.…”

“…L2 speech researchers have used AMT to identify and grade mispronunciations, with the goal of using the crowdsourced data to improve computer-assisted pronunciation training systems (Peabody, 2011;Wang et al, 2013). Researchers have also crowdsourced intelligibility data using transcription tasks as well as accuracy and comprehensibility data using a scalar ratings interface (Loukina et al, 2015;Nagle & Huensch, 2020). Although researchers are increasingly turning to AMT and other online platforms, to date, only one study has reported on the steps needed to design the AMT interface, collect and process the data, and examine its reliability.…”

Beyond Linguistic Features

“…Although comprehensibility has typically been measured through 7-or 9-point Likert scales (e.g., Munro & Derwing, 1995), researchers have occasionally opted for continuous scales over ordinal ones, using a straight line bounded by endpoint descriptors in a paper-and-pencil format (e.g., Isaacs et al, 2015), as in this study, or a slider to record the rating in a computer or online interface (e.g., Saito et al, 2017). Existing scale validation and scale comparison work indicates that there is little difference in the ratings of comprehensibility obtained through scales of various lengths and resolutions (Isaacs & Thomson, 2013), through different scale types (Munro, 2018), or through static or dynamic assessments (Nagle et al, 2019), which implied that the choice of the comprehensibility scale in this study was unlikely to have impacted rating validity.…”

“…Listeners may both understand a speaker and find the speaker easy to understand or may understand a speaker while needing to expend considerable effort. This is the basis for the distinction between intelligibility, a measure of actual understanding, and comprehensibility, listeners' perceived ease of understanding (Munro & Derwing, 1995;Nagle & Huensch, 2020). While intelligibility is a sensible baseline, most L2 speakers want their speech to be easy to understand, a goal that is more closely aligned with the notion of comprehensibility.…”