A multimedia english learning system using hmms to improve phonemic awareness for english learning
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A Multimedia English Learning System Using HMMs to Improve Phonemic Awareness for English Learning. Yen- Shou Lai, Hung-Hsu Tsai and Pao -Ta Yu. Chun-Yu Chen. Outline. Introduction Background The MEL system Speech recognition algorithm of the MEL system

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A Multimedia English Learning System Using HMMs to Improve Phonemic Awareness for English Learning

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A multimedia english learning system using hmms to improve phonemic awareness for english learning

A Multimedia English Learning System Using HMMs to Improve PhonemicAwareness for English Learning

Yen-ShouLai, Hung-Hsu Tsai and Pao-Ta Yu

Chun-Yu Chen


Outline

Outline

Introduction

Background

The MEL system

Speech recognition algorithm of the MEL system

The models of the MEL system

Experiment and results

Discussion and conclusion


Introduction

Introduction

Phonemic awareness can let students more effectively acquire reading and spelling abilities

Teachers in Taiwan often concentrate on speech skills and neglect recognition of phonemic voice

System already exists can not offer learners with learning feedback and high quality of voice

To overcome these problems , this system include :

Dialogue speech tool

Phonemic clustering

Mastery learning theory


Background

Background

The pronunciation difference of EFL learners

Lack: Sounds of some English words do not exist

Substitution: Learners substituted English pronunciation by similar native language

Simplification or complexity: Learners often add or omit one consonant due to side effect of speaking mother tongue

Epenthesis: CVC becomes CVCV


Background1

Background

Mastery learning

well organized teaching materials and effectively managing student’s learning process are two effective instruction factors

can be accomplished by following procedures:

divide the concepts and materials into relatively small and sequential learning units

get the results where the learners have reached the learning level or not , and also to reflect feedback on their learning

Learners who have not mastered a unit, should enter the process of remedial activities or corrections for fully mastering the unit


Background2

Background


Background3

Background

A review of an ASR system based on HMMs

Frame blocking

Feature extraction

HMM


The mel system

The MEL system

MEL system which consists of three modules

Speech Analysis Module

Mastery Learning Module

Management Module

for teachers


Speech recognition algorithm of the mel system

Speech recognition algorithm of the MEL system

Adaptive phoneme clustering (APC) algorithm is used in the design of Speech Analysis Module

APC algorithm includes two steps

Specifies which cluster the input phoneme belongs to

The cluster is decided by K-means algorithm

The input phoneme is recognized by involving all HMMs in the specified cluster

Phonemes classified into clusters to form a hierarchical recognition model with two levels


Speech recognition algorithm of the mel system1

Speech recognition algorithm of the MEL system

Training HMM model with APC algorithm

Frame has a set of feature parameters

= (, ,……. )

the training-pattern set can be expressed as a form

= (| j=1,2….,….n )

after feeding the K-means algorithm with the set, a new training-pattern setcan be obtained and represented by

= (| i=1,2,….j=1,2….,….n )


Speech recognition algorithm of the mel system2

Speech recognition algorithm of the MEL system

is employed to construct a classification model which is realized by HMMs


Speech recognition algorithm of the mel system3

Speech recognition algorithm of the MEL system

Confusing phoneme

A set of confusing phonemes is specified because some phonemes apparently exist in more than one cluster

Reconstructed the 2-level HMMs model with (+1) clusters for the confusing phonemes

is extended toby replacing with (

if is a feature of a confusing phoneme

A phoneme is considered as a confusing phoneme if its error rate

/ exceeds a threshold


The models of the mel system

The models of the MEL system

Speech analysis module

Speech Analysis Module is used to analyze the speech signal and to detect the correction of student’s pronunciation

the input speech signal is evaluated in terms of the four factors:

Pronunciation

Intonation

Tempo

Volume

For the pronunciation , the interval of the voice signal to analyze the pronunciation error if the pronounce score is less than 60


The models of the mel system1

The models of the MEL system

For the intonation, the MEL system provides the intonation curves of the teacher with students

For the analysis of tempo, the system can compare the voice speed every student reads with teachers


The models of the mel system2

The models of the MEL system

For the analysis of volume, the MEL system compares the volume of students with teacher’s voice for their volume stress


The models of the mel system3

The models of the MEL system

Mastery learning module for students

The students’ learning module includes

Learning Material

Self-test

Error Analysis

Video Camera

Record Learning History

Query for learning history

Management module for teachers

This module can be used to manage instructional materials, manage students’ profiles, assess students’ tests, and query students’ learning histories


Experiment and results

Experiment and results

Participants

120 third-grade students from an elementary school, as the experimental group and the control group

Use the phonemic awareness scores to classify students into three categories

Assessment materials

Phonemic awareness test

Learning achievement test


Discussion and conclusion

Discussion and conclusion

The APC method is employed in the design of the MEL system for reducing the computation time of the hierarchical HMMs

The MEL system can promote the phonemic ability of the students with the middle and the low phonemic ability

The MEL system can interactively provide concrete feedbacks. It is helpful for self-regulated learning


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