Improving language learning for non-native speakers

Improving language learning for non-native speakers

Improving language learning for non-native speakers Xinyu Tang, Allen Parish, Steven Chang Introduction Speech processing and language learning Learning a second language is difficult

Complicated by biases introduced by native language Exaggeration may help to recognize the subtle difference between sounds Na La Background LPC analysis can capture core sound components Exaggeration can actuate differences between sounds

Goals/Objectives Overarching Goal Help non-native speakers distinguish confusing sounds Project Objectives Implement

existing work as a feasibility test Use LPC-based analysis to separate a cluster of similar words Use LPC-based analysis to separate adjacent phonemes Related Work Concept of LPC Introduced by [Makhoul 1978] [Kahn 1998] used LPC Exaggeration

[Protopapas 1998] used LPC extrapolation for non-native speakers Tools for phoneme separation SFS Sphinx Proposed solution 1. Standard Pairwise Exaggeration [Protopapas 1998] Knock and Lock Cop and Cup

2. Split difficult clusters of words Thud, dog, god, sod, thought, cod, thus Exaggerate from mean Exaggerate from K - nearest neighbors (KNN) Split sounds, normalize LPC Analysis

Exaggeration/Interpolation 3. Sequential Exaggeration Artificial, Probability LPC Synthesis Re-assemble sounds Standard Pairwise Results

Exaggerated sounds helps Xinyu to distinguish La and Na Interpolation make sounds hard to distinguish Extreme exaggeration make sounds distorted and hard to distinguish Classification rate Na La Na

La Exaggerate from cluster center Those exaggerated sounds are further from each other Exaggerated Ra Original Na Euclidean distances of sounds Original

Exaggerated La Distributions of sounds in LDA projection Exaggerate from Cluster Center Thud, dog, god, sod, thought, cod, thus Exaggerate sounds from the middle A little bit crowded for big cluster

Euclidean distances of sounds Distributions of sounds in LDA projection KNN Exaggeration From Closest Neighbors-2NN Nearer sounds are more exaggerated Euclidean distances of sounds Distributions of sounds in LDA projection Sequential Exaggeration

Artificial Original Exaggerated Probability Original Exaggerated Analysis of Results LPC-based exaggeration succeeds in exaggerating similar sounds

Exaggeration can help/hinder people distinguish ambiguous sounds Difficulties The feeling of sounds are subjective Hard to exaggerated sounds Natural It is hard to find subjects who cant distinguish two original sounds

Phoneme separation is a difficult and inexact task Future Work Better testing of subjects that have difficulties with particular sounds Work in conjunction with linguists to apply approaches to known difficult phonemes Automate entire process as a training tool for non-native speakers Conclusions LPC-based exaggeration can help people differentiate tough phonemes Our results demonstrate feasibility of a

variety of approaches Which technique to use is still a trial and error process References Source Code Interval Toolbox http://rvl4.ecn.purdue.edu/~malcolm/interval/1998-010/ Speech Filing System - http://www.phon.ucl.ac.uk/resource/sfs/

Original Paper Modified LPC resynthesis for controlling speech stimulus discriminability. 136th Annual Meeting of the Acoustical Society of America. Norfolk, VA, 13-16 October. [In Journal of the Acoustical Society of America 104 (3 Pt. 2): 1855]

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