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- W3195384016 abstract "Much evidence has shown that first language (L1) playsan important role in the formation of L2 phonological system duringsecond language (L2) learning process. This combines with the factthat different L1s have distinct phonological patterns to indicatethe diverse L2 speech learning outcomes for speakers from differentL1 backgrounds. This dissertation hypothesizes that phonologicaldistances between accented speech and speakers' L1 speech are alsocorrelated with perceived accentedness, and the correlations arenegative for some phonological properties. Moreover, contrastivephonological distinctions between L1s and L2 will manifestthemselves in the accented speech produced by speaker from theseL1s. To test the hypotheses, this study comes up with acomputational model to analyze the accented speech properties inboth segmental (short-term speech measurements on short-segment orphoneme level) and suprasegmental (long-term speech measurements onword, long-segment, or sentence level) feature space. The benefitof using a computational model is that it enables quantitativeanalysis of L1's effect on accent in terms of differentphonological properties. The core parts of this computational modelare feature extraction schemes to extract pronunciation and prosodyrepresentation of accented speech based on existing techniques inspeech processing field. Correlation analysis on both segmental andsuprasegmental feature space is conducted to look into therelationship between acoustic measurements related to L1s andperceived accentedness across several L1s. Multiple regressionanalysis is employed to investigate how the L1's effect impacts theperception of foreign accent, and how accented speech produced byspeakers from different L1s behaves distinctly on segmental andsuprasegmental feature spaces. Results unveil the potentialapplication of the methodology in this study to providequantitative analysis of accented speech, and extend currentstudies in L2 speech learning theory to large scale. Practically,this study further shows that the computational model proposed inthis study can benefit automatic accentedness evaluation system byadding features related to speakers' L1s." @default.
- W3195384016 created "2021-08-30" @default.
- W3195384016 creator A5004787804 @default.
- W3195384016 date "2018-01-01" @default.
- W3195384016 modified "2023-09-27" @default.
- W3195384016 title "A computational model for studying L1’s effect on L2 speech learning" @default.
- W3195384016 hasPublicationYear "2018" @default.
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