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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

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Improving language learning for non-native speakers

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  1. Improving language learning for non-native speakers Xinyu Tang, Allen Parish, Steven Chang

  2. 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

  3. Background • LPC analysis can capture core sound components • Exaggeration can actuate differences between sounds

  4. 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

  5. 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

  6. Proposed solution Split sounds, normalize • 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 LPC Analysis Exaggeration/Interpolation LPC Synthesis Re-assemble sounds

  7. 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 La Na La Na

  8. Exaggerate from cluster center • Those exaggerated sounds are further from each other Exaggerated Ra Original La Na Euclidean distances of sounds Distributions of sounds in LDA projection Exaggerated Original

  9. 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

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

  11. Sequential Exaggeration • Artificial • Probability Original Exaggerated Original Exaggerated

  12. Analysis of Results • LPC-based exaggeration succeeds in exaggerating similar sounds • Exaggeration can help/hinder people distinguish ambiguous sounds

  13. 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

  14. 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

  15. 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

  16. 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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