Deconstructing comprehensibility: identifying the linguistic influences on listeners' L2 comprehensibility ratings

Comprehensibility, a major concept in second language (L2) pronunciation research that denotes listeners’ perceptions of how easily they understand L2 speech, is central to interlocutors’ communicative success in real-world contexts. Although comprehensibility has been modeled in several L2 oral proficiency scales—for example, the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS)—shortcomings of existing scales (e.g., vague descriptors) reflect limited empirical evidence as to which linguistic aspects influence listeners’ judgments of L2 comprehensibility at different ability levels. To address this gap, a mixed-methods approach was used in the present study to gain a deeper understanding of the linguistic aspects underlying listeners’ L2 comprehensibility ratings. First, speech samples of 40 native French learners of English were analyzed using 19 quantitative speech measures, including segmental, suprasegmental, fluency, lexical, grammatical, and discourse-level variables. These measures were then correlated with 60 native English listeners’ scalar judgments of the speakers’ comprehensibility. Next, three English as a second language (ESL) teachers provided introspective reports on the linguistic aspects of speech that they attended to when judging L2 comprehensibility. Following data triangulation, five speech measures were identified that clearly distinguished between L2 learners at different comprehensibility levels. Lexical richness and fluency measures differentiated between low-level learners; grammatical and discourse-level measures differentiated between high-level learners; and word stress errors discriminated between learners of all levels

Children at risk : their phonemic awareness development in holistic instruction

Includes bibliographical references (p. 17-19

Disentangling accent from comprehensibility

The goal of this study was to determine which linguistic aspects of second language speech are related to accent and which to comprehensibility. To address this goal, 19 different speech measures in the oral productions of 40 native French speakers of English were examined in relation to accent and comprehensibility, as rated by 60 novice raters and three experienced teachers. Results showed that both constructs were associated with many speech measures, but that accent was uniquely related to aspects of phonology, including rhythm and segmental and syllable structure accuracy, while comprehensibility was chiefly linked to grammatical accuracy and lexical richness

A computational model for studying L1’s effect on L2 speech learning

abstract: Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. To test the hypotheses, this study comes up with a computational model to analyze the accented speech properties in both segmental (short-term speech measurements on short-segment or phoneme level) and suprasegmental (long-term speech measurements on word, long-segment, or sentence level) feature space. The benefit of using a computational model is that it enables quantitative analysis of L1's effect on accent in terms of different phonological properties. The core parts of this computational model are feature extraction schemes to extract pronunciation and prosody representation of accented speech based on existing techniques in speech processing field. Correlation analysis on both segmental and suprasegmental feature space is conducted to look into the relationship between acoustic measurements related to L1s and perceived accentedness across several L1s. Multiple regression analysis is employed to investigate how the L1's effect impacts the perception of foreign accent, and how accented speech produced by speakers from different L1s behaves distinctly on segmental and suprasegmental feature spaces. Results unveil the potential application of the methodology in this study to provide quantitative analysis of accented speech, and extend current studies in L2 speech learning theory to large scale. Practically, this study further shows that the computational model proposed in this study can benefit automatic accentedness evaluation system by adding features related to speakers' L1s.Dissertation/ThesisDoctoral Dissertation Speech and Hearing Science 201

Native English speakers\u27 perceptions of intelligibility in the extended discourse produced by non-native speakers

In pronunciation, segmental accuracy has been recognized as an important aspect in contributing to a non-native speaker\u27s intelligibility. Nonetheless, there has been a lack of research focusing on the role of segmental errors in understanding extended discourse. Furthermore, previous research studies on intelligibility have largely been conducted in a controlled condition where a listener\u27s cognitive process is more limited than in a real-life setting. In addition, proficiency level has not been considered as one of the factors contributing the intelligibility of non-native speech. This thesis uses a think-aloud methodology to investigate how native English speakers perceived how segmental errors contributed to reduced intelligibility of academic discourse produced by three Korean speakers with varying oral proficiency.;Five native American English listeners watched the teaching demonstrations performed by the three Korean speakers of English who had been rated as being at the advanced, intermediate and beginner levels or oral proficiency in English. While listening, the native speakers paused whenever they encountered a communicative breakdown and described the nature of the breakdown in understanding. Both quantitative and qualitative analyses were conducted. Listener results for the numbers of communicative breakdowns, and the numbers of the locations where communicative breakdowns were associated with segmental errors, were compared. The types of segmental errors identified by the listeners were analyzed to determine which errors appeared to impact intelligibility the most.;The findings showed that the listeners had communicative breakdowns for different reasons depending on proficiency levels. For the lower-level speakers, listeners stopped most often for segmental errors, whereas they stopped for non-phonological reasons as often as for segmental errors while listening to the advanced speaker. Also, not all segmental features were equally important for the listeners in this study. The consonants in syllable final position seemed to be important for understanding extended discourse on an academic topic. The comparison between the intermediate and beginner speakers suggested that not only was pronunciation important, but also that non-pronunciation related factors were important in being perceived as an effective speaker in an academic context. For nonnative speakers of English, and for Korean speakers of English in particular, a pedagogical approach which prioritizes certain segmental features depending on proficiency level is suggested

Automated assessment of second language comprehensibility: Review, training, validation, and generalization studies

Whereas many scholars have emphasized the relative importance of comprehensibility as an ecologically valid goal for L2 speech training, testing, and development, eliciting listeners’ judgments is time-consuming. Following calls for research on more efficient L2 speech rating methods in applied linguistics, and growing attention toward using machine learning on spontaneous unscripted speech in speech engineering, the current study examined the possibility of establishing quick and reliable automated comprehensibility assessments. Orchestrating a set of phonological (maximum posterior probabilities and gaps between L1 and L2 speech), prosodic (pitch and intensity variation), and temporal measures (articulation rate, pause frequency), the regression model significantly predicted how naïve listeners intuitively judged low, mid, high, and nativelike comprehensibility among 100 L1 and L2 speakers’ picture descriptions. The strength of the correlation (r = .823 for machine vs. human ratings) was comparable to naïve listeners’ interrater agreement (r = .760 for humans vs. humans). The findings were successfully replicated when the model was applied to a new dataset of 45 L1 and L2 speakers (r = .827) and tested under a more freely constructed interview task condition (r = .809)