Paper Reading AI Learner

Spelling provides a precise phonological target. Orthography and acoustic variability in second language word learning

2021-09-08 08:27:47
Pauline Welby, Elsa Spinelli, Audrey Bürki

Abstract

L1 French participants learned novel L2 English words over two days of learning sessions, with half of the words presented with their orthographic forms (Audio-Ortho) and half without (Audio only). One group heard the words pronounced by a single talker, while another group heard them pronounced by multiple talkers. On the third day, they completed a variety of tasks to evaluate their learning. Our results show a robust influence of orthography, with faster response times in both production (picture naming) and recognition (picture mapping) tasks for words learned in the Audio-Ortho condition. Moreover, formant analyses of the picture naming responses show that orthographic input pulls pronunciations of English novel words towards a non-native (French) phonological target. Words learned with their orthographic forms were pronounced more precisely (with smaller Dispersion Scores), but were misplaced in the vowel space (as reflected by smaller Euclidian distances with respect to French vowels). Finally, we found only limited evidence of an effect of talker-based acoustic variability: novel words learned with multiple talkers showed faster responses times in the picture naming task, but only in the Audio-only condition, which suggests that orthographic information may have overwhelmed any advantage of talker-based acoustic variability.

Abstract (translated)

URL

https://arxiv.org/abs/2109.03490

PDF

https://arxiv.org/pdf/2109.03490.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