Improving language learning for non-native speakers Xinyu Tang, Allen Parish, Steven Chang.
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Transcript of Improving language learning for non-native speakers Xinyu Tang, Allen Parish, Steven Chang.
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 GoalHelp non-native speakers distinguish confusing
sounds Project Objectives
Implement existing work as a feasibility testUse LPC-based analysis to separate a cluster of
similar wordsUse 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) 3. Sequential Exaggeration
Artificial, Probability
Split sounds, normalize
LPC Analysis
Exaggeration/Interpolation
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
Na La Na La
Cla
ssifi
catio
n ra
te
Exaggerate from cluster center
Those exaggerated sounds are further from each other
LaNa
Ra
Euclidean distances of sounds Distributions of sounds in LDA projection
Original
Exaggerated
Original Exaggerated
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
Probability
Original
Exaggerated
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 can’t 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 CodeInterval 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]