WEB-BASED INTELLIGENT COMPUTER-ASSISTED LANGUAGE LEARNING SYSTEM FOR YORÙBÁ(YiCALL)

953
WEB-BASED INTELLIGENT COMPUTER-ASSISTED
LANGUAGE LEARNING SYSTEM FOR YORÙBÁ(YiCALL)
Odetunji A. Odejobi1 & Tony J. Beaumont2
Computer Science Department
School of Engineering and Applied Science
Aston University
Birmingham, B4 7ET
United Kingdom
ABSTRACT
In this presentation, we describe a web-based Intelligent Computer Assisted Language Learning (iCALL) system for the
learning of Yoruba Language (YiCALL). YiCALL development is based on the integration of ideas from computer aided
education; computer mediated communication as well as techniques in artificial intelligence. The system is designed for
access over the internet. Various design and implementation issues with respect to components of the system are here
discussed and the direction of ongoing work highlighted.
KEYWORDS
e-learning, speech synthesis and recognition, CALL
1. INTRODUCTION
Computer-Assisted Language Learning (CALL) provides the basic technology for assisting language learners
to acquire important communication skills in a given language. Recent advances in Computer Mediated
Communication (CMC), CALL, and World Wide Web (WWW) facilitates the integration of these
technologies in the development of powerful language education systems . We present a general framework
underlying a pioneering research work -to the best of our knowledge this is first such project on Yorùbá
language- focused on the development of a web-based intelligent CALL (iCALL) system for Yorùbá
language. It is important that a CALL system possess the ability to adapt its behaviour to the goals, tasks,
interests, and specific needs of individual users or groups of users (Brusilovsky, 2002). The central goal of
modern approached to language learning and teaching includes, communicative language teaching, goaloriented
learning and process approach to writing. Basically, language learning strategies seek to enhance
student’s autonomy and control over the learning process (Warshauer, et al, 1996). Since speech and writing
are the basic media of human communication, a CALL system that exploits them would provide a better
language learning environment. This paper provides an overview of ongoing research into the development
of an intelligent web-based iCALL for Yorùbá.
Yorùbá is one of the four major languages spoken in Africa. Other languages in this category include
Arabic, Hausa, and Swahili. In Nigeria, Yorùbá is one of the three major native languages (Hausa and Igbo)
spoken alongside English, which is the official language. In Nigeria, the homeland of Yorùbá lies between
longitudes 20 30’and 6030’ East of the Meridian and Latitudes 60 and 90 North of the Equator (CIA, 2001).
Yorùbá is the native language of people in Lagos, Oyo, Ogun, Ondo, Ekiti, and Osun states of Nigeria. It is
also spoken in some part of Edo, and Kogi states of Nigeria as well as in Central Togo, East Central part of
Republic of Benin and in Sierra Leone (where it is called Aku). There are 25 letters in the Yorùbá language
alphabet. This is made up of 18 consonants (b, d, f, g, gb, h, j, k,l , m, n, p, r, s, s, t, w, y) and seven vowels (a,
e,?, i, o, o, u). There are five nasalized vowels in the language (an, en, in, on, un). Yorùbá is a tone language
with 3 contrastive tone and 2 allotones. There are about 30 million speakers of Yorùbá language in the South
Western part of Nigeria. Students cite many reasons for studying Yoruba, including personal interest in West
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African cultures, research interests, and fulfilment of foreign language requirements (CIA, 2001). African-
American students often study Yorùbá out of interest in their own heritage, since many of the slaves brought
to North America during the 18th and 19th centuries came from Yorùbá -speaking areas (Ajolore, 1974).
2. OVERVIEW OF YiCALL ARCHITECTURE
The basic configuration of YiCALL is as shown in Figure 1. There are three basic modules in the system
architecture namely; user interface, language resource, and intelligent learning control modules. The user
interface module comprises; (1) Automatic Speech Recognition (ASR), (2) Text -to-Speech (TTS) synthesis
and (3) Natural Language Processing (NLP) sub-modules. The language resource module comprise of the
orthography (or written) and voice knowledge base and language curriculum. The intelligent control module
control and coordinates the learning process based on some evaluation criteria that takes account of student’s
ability. Each of the technologies applied in this work have been used to develop commercial applications, but
they still have some limitations (Kohler, 2001).
Figure 1. The overview of architecture Yorùbá iCALL prototype
2.1 YiCALL User interface
The TTS sub-module implements the text -to-speech conversion task in the user interface by converting
Yoruba text , typed by learners, into synthetic speech. Spoken equivalent of system response could also be
generated while displaying corresponding texts. To provide a flexible learning environment, the speech
synthesis process is based on the concatenation of tone-syllable units from a pre-recorded and annotated
speech corpus (Lee and Vox, 2002). Information extracted in respect of the syntax and semantics of input
sentences are used to generate the intonation and rhythm of the synthesised utterance. Since semantics and
pragmatic analysis of unrestricted text is difficult, heuristic methods are being applied in determining the
accent and phrase structure which are important in determining prosodic parameter of the synthetic speech
(Portele and Barbara, 1997; Black et al, 1996).
The ASR sub-module serves as the voice interface to capture learner’s pronunciations. It also provides
voice feedback during learning. ASR technology provides the means for the tutoring system to capture the
voice of the learner. Features are extracted from captured voice signal and used to determine what was
spoken. The substantial progress achieved in automatic speech recognition in the past two decades has led to
User Interface
Yorùbá Speech
Synthesis Module
Speaker Independent
Speech Recognition
Module
Data and
Knowledgebase
Interface
Speech
Knowledge
Database
Language Learning
Curriculum Database
Intelligent learning Control
Learner
model
Natural Language
Processing Module
Learning Evaluation
WEB-BASED INTELLIGENT COMPUTER-ASSISTED LANGUAGE LEARNING SYSTEM FOR
YORÙBÁ(YiCALL)
955
a variety of successful demos and some commercial products using speech technology. In a CALL
environment, where potential users may be non-native speakers of the language, ASR systems have to deal
with variety of speaker accents. Result of research in multilingual recognition and spoken dialog systems
(Adda-Decker, 2001; Kohler, 2001) is being exploited for solving this problem.
The Natural Language Processing (NLP) module provides the formal framework for modelling aspects of
the syntactic and semantic structure of Yorùbá language. A trigram language model of Yorùbá based on the
Hidden Markov Model (HMM) is being developed. NLP techniques, such as parsing and semantic analysis,
play important role within language tutoring systems (Kupiec, 1992). Holland and Kaplan (1995) have
discussed the significant trends in the exploitation of these techniques, design issues and tradeoffs, as well as
current and potential contributions of NLP technology with respect to instructional theory and educational
practice. We intend to annex NLP tools and techniques in providing an effective language model for
YiCALL.
2.2 Language resource and intelligent control
Text and speech corpuses emanating from two local newspapers and their spoken equivalents, recorded by an
adult male native-speaker, form the basic language resource. The content area selected for the learning is the
Yorùbá greeting environment. The basic structure for greeting is ; {Situation/event}. That is , the word
before an event or situation signifies a greeting. The context and situation of various Yorùbá greeting
were compiled into the curriculum of six lessons. Each lesson has four levels and the level selected for
learning is dependent on learner’s profile. The Knowledge and Database Interface (KDI) selects learning
module and exercises, interpret learner’s input, and compiles appropriate response to guide the learner. The
KDI is design around object oriented model. It contains a structured curriculum for Yorùbá language as well
as those for learning the alphabet, phonology, morphology and phonetics of the language. The language
resource is designed in line with standard speech application language resource requirement (Holland and
Kaplan, 1995).
The activities of the KDI and the user interface are under the control of the Intelligent Learning Control
(ILC) sub-module. The ICL controls the learning process based on the learner’s model and level of
proficiency. It determines what module to present to learner and control the activity of the speech generation
and recognition process.
3. LEARNING AND LEARNER’S MODEL
The learner model stores the characteristics of the learner relevant to the system’s tutoring strategies. The
learner model defined in the system specifications comprised of data objects which describe the following
parameters; (i) personal details of the learner, (ii) the system estimates of learner’s grammatical and oral
proficiency in Yorùbá and (iii) a function describing the relative stable characteristics of the learner. The
learner’s model is updated via the parsing and analysis of contextual information which includes error
classes, potential causes of errors, the response strategies selected by the tutoring module and the level of
help that was sought by the learner.
To implement the tutoring process, the learning prototype is based on two strategies, namely;
Reinforcement learning, for drilling and proficient learning stage and Learning by analogy, for introductory
and intermediate learning stage. A five-tuple finite state automaton was used to model these learning process
as follows; Learner:= < SL, Sp, Su, SF, Ss >. Where: Sp is the present state; Su is the set of possible learning
units, SL is the set of possible learning states; SF is the final or desirable learning state, Ss is a step in the
strategy. In this context then, Sp, SFÎSL; Ss: Sp × Su¾¾® Sp and Sp=Ss(Sp, Su). Thus, using the present
learning unit and applying the next unit step in the leaning strategy to the present state will make the learning
to move to another state in the learning process, say Spi. If Spi = Sf then the learning process is complete and
the learner is expected to have achieved a predefined communication proficiency in the language.
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4. IMPLEMENTATION
Implementation of the learning, language and user models are specified using finite-state compilers and
algorithms, and the results are stored as finite-state transducers. Creating, validating and verifying the
proposed implementation specification is an ongoing work. The information flow in the web-based
implementation of YiCALL is as shown in Figure 2. The aim is to make learners have access to YiCALL
using any WWW browser. At present we are experiment with SALT (Intel, 2002:http://www.saltforum.org),
Speech Application Language Tag, which is a mark up language for implementing speech interface. Other
optimization and customization programmes to make YiCALL easily accessible via a WWW browser would
be developed around Java Jdeveloper toolkits.
Figure 2. Information flow in web-based- implementation of YiCALL.
5. SUMMARY, CONCLUSION, AND ONGOING WORK
To facilitate a flexible and yet user friendly CALL, the system should, as much as possible, exploit available
medium of communication in natural learning process. There are two media of communication in natural
learning environment, namely; speech and writing. In a flexible and goal oriented learning environment, it
should be possible for the learner to interact with the computer using speech and writing. The focus of
current work is the computational analysis, design and implementation of YiCALL based on the integration
of ideas from speech synthesis , speech recognition and artificial intelligence. At the same time we seek to
make the system widely available via the internet. The limitations of speech recognition, speech synthesis
and natural language processing as well as the inherent problem of integrating the system with AI techniques
is generating unique challenge in the design and implementation of the proposed system.
ACKNOWLEDGEMENT
The contributions of the Commonwealth Scholarship Commission in United Kingdom and The British
Council to this research are hereby acknowledged.
REFERENCES
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WEB
Browser
(Java)
YiCALL
Yoruba
Learner