Technology in Education

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17 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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EE3P BEng Final Year Project


1
st

meeting

SLaTE


Speech and Language
Technology in Education

Martin Russell

m.j.russell@bham.ac.uk

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
2

Introduction


SLaTE


Resources


Some possible projects


Phase 1 of project


Things that you need to know


Any questions?

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
3

SLaTE


Speech and Language Technology in Education
(SLaTE)


Concerned with
any

aspect of applying speech and
language technology in education


Most common applications are language related:


Interactive intelligent second language (L2) learning


Interactive reading tutors


Interactive pronunciation tutors (L1 or L2)


EE3P


BEng Final Year Project 2009
-
2010
-

Slide
4

SLaTE


But, SLaTE is
not

restricted to language
learning:


Spoken language interaction with any
educational system (e.g. dictating
mathematical formulae)

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
5

Resources


SLaTE is a relatively new topic, but there are
some resources:


BEng FYP 2009
-
2010 web page


http://www.eee.bham.ac.uk/russellm/ee3p
-
fyp09_files/EE3P
-
BEng
-
FYP2009.pdf


Proceedings of the SLaTE 2007 and 2009
workshops


SLaTE 2007


SLaTE 2009

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
6

Timetable


Autumn term


Group meetings


Learn background


‘Mini
-
project’


Choose main project


First ‘bench inspection’ (5 minutes)


Spring term


Individual project


Individual meetings


Final bench inspection (weeks 10/11)


Final report


Summer term


Present poster at ‘Project Open Day’

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
7

Possible Projects


Measuring Goodness of Pronunciation (GoP)


Dictation of mathematical expressions


Selection of appropriate audio material for
SLaTE based on acoustic analysis


Measuring reading fluency and its relationship
with reading ability


Selection of appropriate audio material for
SLaTE based on lexical analysis

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
8

1. Measuring Goodness of Pronunciation
(
GoP
)


Use Automatic Speech Recognition technology to decide whether a
pronunciation of a given word by a learner of English as a second
language is acceptable. The learner’s utterance is compared with an
acoustic statistical model
and accepted or rejected based on a
score. By restricting the vocabulary appropriately it will be possible
to develop a simple GoP system using the HTK speech recognition
toolkit. This project should include a serious evaluation of the
system.


EE3P


BEng Final Year Project 2009
-
2010
-

Slide
9

2. Dictation of mathematical expressions


This project involves the use of automatic speech recognition
technology to dictate mathematical equations. The project will
involve collecting data to determine how engineers say
mathematical expressions, and then developing techniques to
parse this data. For example, do engineers always say when
‘brackets’ are needed, or can the need for brackets be inferred
from pause duration? A simple real
-
time demonstrator will be
developed using a commercial speech recognition system and
evaluated using carefully prepared test data.

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
10

3. Selection of appropriate audio material for
SLaTE

based on acoustic analysis


Different voices are appropriate for different applications. For
example, a voice which is suitable for reading stories to children
would be inappropriate for communicating instructions to soldiers
on a parade ground! The goal of this project is to find out if it is
possible to use techniques from automatic speech recognition to
differentiate between audio material on the web which is suitable
or unsuitable for SLaTE applications with children. The decision
should be based on properties of the acoustic signal rather than
lexical or syntactic content. An important part of the project is the
evaluation and critical analysis of the system

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
11

4. Selection of appropriate audio material for
SLaTE

based on lexical analysis


This project is related to Project 3, but in this case the goal is to
determine the suitability of a piece of audion for SLaTE
applications with children using lexical (word content) and
syntactic (grammatical) analysis. This will involve passing the
audio through a commercial speech recognition system and
analyzing the result. Important questions will include whether or
not current speech recognition technology is sufficiently accurate
to support this application, and the effect of speech recognition
accuracy on system performance.

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
12

5. Measuring reading fluency and its
relationship with reading ability



The goal of this project is to design, implement and test a system
for measuring, automatically, reading fluency. The system might
measure factors such as the number of words spoken per minute,
or the average length of pauses. The system will be based on a
commercial automatic speech recognition system. The student
will collect a set of recordings of people reading with different
levels of proficiency, and this data will be used to test the system.
In addition, the student will obtain human judgments of the
reading proficiency shown in each of the recordings and discover
whether these subjective judgments correlate with the measures
of fluency.

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
13

Phase 1 of project (autumn term)


Understand basic speech and language technology
used in
SLaTE



Implement a simple
SLaTE

system (see next slide)


Choose individual project for the Spring term


‘Deliverables’


Knowledge of speech and language technology


Simple software demonstration (of something)


Individual project specification

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
14

Phase 1


possible project


Implement (and test) a system that takes texts
from the internet and decides whether they are
appropriate for a particular level of learner of
English as a second language.


See the paper by
Heilman, Zhao, Pino and
Eskenazi

(FYP 2009/10 web page)


...or you could choose something which is the
first phase of your own project

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
15

Things you need to know


Automatic speech recognition:


How does a basic system work?


What is a hidden Markov model (HMM)?


What is HTK?


We will discuss this at the next meeting

EE3P


BEng Final Year Project 2009
-
2010
-

Slide
16

Any Questions?