Paper Reading AI Learner

Chinese Learners' Phonetic Transfer of /i/ from Mandarin Chinese to General American English: Evidence from Perception and Production Experiments

2021-12-27 08:45:34
Lintao Chen

Abstract

Ever since the development of Contrastive Analysis (CA) in the 1950s, which focuses on comparing and contrasting two language systems, linguists have started to systematically explore the influence of the mother tongue on acquiring a second language. This phenomenon is later defined as "language transfer". The current paper concerns language transfer at the phonetic level and concentrates on the transfer phenomenon existing in advanced-level Chinese learners' acquisition of English vowels /i/ and its lax counterpart. By determining whether advanced-level Chinese English-language learners (ELLs) can accurately distinguish between /i/ and its lax counterpart, and pronounce them in English words precisely, this paper serves as a reference for further studying Chinese ELLs' language transfer. Two objectives were to be met: firstly, learners' perceptual ability to distinguish between vowels /i/ and its lax counterpart should be examined; and secondly, the effect of the phonetic transfer should be determined. A perception test and a production test were used to attain these two objectives. Both tests were completed by 12 advanced-level Chinese ELLs, six males and six females. Results indicate that both male and female participants could consciously distinguish between /i/ and its lax counterpart. All participants have signs of experiencing negative phonetic transfer in their pronunciation, except that the current data do not decisively reflect an impact of the phonetic transfer on female ELLs' acquisition of the high front lax vowel in English words.

Abstract (translated)

URL

https://arxiv.org/abs/2112.13571

PDF

https://arxiv.org/pdf/2112.13571.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot