Phonological acquisition in the French-English interlanguage
-
Adrien Meli
Abstract
This paper aims at providing insight into the sort of information that different protocols and elicitation methods may yield on the properties of the French-English interlanguage, and into how accurately the data found is predicted by known Second Language Acquisition (SLA) theoretical frameworks. Models such as Flege’s Speech Learning Model or Best’s Perceptual Assimilation Model make various predictions on phonemic acquisition based on phonological structural symmetries – or absence thereof – between the source language and the target language. However, this preliminary study of the acquisition of /ɪ/-/iː/, /ʊ/-/uː/ and /θ/-/ð/ argues that these assumptions fail to predict differences in learning patterns between sets of phonemes pertaining to the same cross-language structure (e.g. English /ɪ/-/iː/ and /ʊ/-/uː/, corresponding to French /i/ and /u/ respectively) and calls for including parameters such as phonotactics, L2- specific frequency of occurrence and lexical contrast in model predictions. The material used consists in subsets of first-year, third-year and fourth-year students of English recorded as part of an examination for the completion of their courses.
Abstract
This paper aims at providing insight into the sort of information that different protocols and elicitation methods may yield on the properties of the French-English interlanguage, and into how accurately the data found is predicted by known Second Language Acquisition (SLA) theoretical frameworks. Models such as Flege’s Speech Learning Model or Best’s Perceptual Assimilation Model make various predictions on phonemic acquisition based on phonological structural symmetries – or absence thereof – between the source language and the target language. However, this preliminary study of the acquisition of /ɪ/-/iː/, /ʊ/-/uː/ and /θ/-/ð/ argues that these assumptions fail to predict differences in learning patterns between sets of phonemes pertaining to the same cross-language structure (e.g. English /ɪ/-/iː/ and /ʊ/-/uː/, corresponding to French /i/ and /u/ respectively) and calls for including parameters such as phonotactics, L2- specific frequency of occurrence and lexical contrast in model predictions. The material used consists in subsets of first-year, third-year and fourth-year students of English recorded as part of an examination for the completion of their courses.
Chapters in this book
- Prelim pages i
- Table of contents v
-
Section 1. Introduction
- Introduction 3
- Learner corpora 9
-
Section 2. Compilation, annotation and exchangeability of learner corpus data
- Developing corpus interoperability for phonetic investigation of learner corpora 33
- Learner corpora and second language acquisition 65
- Competing target hypotheses in the Falko corpus 101
-
Section 3. Automatic approaches to the identification of learner language features in learner corpus data
- Using learner corpora for automatic error detection and correction 127
- Automatic suprasegmental parameter extraction in learner corpora 151
- Criterial feature extraction using parallel learner corpora and machine learning 169
-
Section 4. Analysis of learner corpus data
- Phonological acquisition in the French-English interlanguage 207
- Prosody in a contrastive learner corpus 227
- A corpus-based comparison of syntactic complexity in NNS and NS university students’ writing 249
- Analysing coherence in upper-intermediate learner writing 265
- Statistical tests for the analysis of learner corpus data 287
- Index 311
Chapters in this book
- Prelim pages i
- Table of contents v
-
Section 1. Introduction
- Introduction 3
- Learner corpora 9
-
Section 2. Compilation, annotation and exchangeability of learner corpus data
- Developing corpus interoperability for phonetic investigation of learner corpora 33
- Learner corpora and second language acquisition 65
- Competing target hypotheses in the Falko corpus 101
-
Section 3. Automatic approaches to the identification of learner language features in learner corpus data
- Using learner corpora for automatic error detection and correction 127
- Automatic suprasegmental parameter extraction in learner corpora 151
- Criterial feature extraction using parallel learner corpora and machine learning 169
-
Section 4. Analysis of learner corpus data
- Phonological acquisition in the French-English interlanguage 207
- Prosody in a contrastive learner corpus 227
- A corpus-based comparison of syntactic complexity in NNS and NS university students’ writing 249
- Analysing coherence in upper-intermediate learner writing 265
- Statistical tests for the analysis of learner corpus data 287
- Index 311
