design an expressive intelligent agent to interacting with ... 80.pdf · it can be a friend or a...

Design an Expressive Intelligent Agent to Interacting With

Humans

M. Manuruzzaman, L. Alam, M. Moshiul Hoque*and M. S. Arefin

Department of Computer Science & Engineering, CUET, Chittagong, Bangladesh

*E-mail: [email protected]

Abstract: An expressive agent can provide social support and wellness coaching to isolated older adults. It can

provide companionship dialogue, game co-play, and social act ivity tracking. It is necessary because it can play a

vital ro le to reduce human loneliness and sadness to keep them happy. Abundant HCI studies address the

challenges that should be focused in designing virtual interactive agent. In this paper, we present an intelligent

interactive virtual agent that can provide seven basic facial expressions while interacting with it via text input.

The proposed agent is capable to communicate with people by showing some basic facial expression such as joy,

sad, fear, surprise, anger, contempt, disgust respectively that are useful to reduce their loneliness for a while.

Evaluation results in terms of subjective and quantitative ways reveals that the performance of the proposed

agent is quite satisfactory.

Keywords: Human computer interaction, expressive agent, parsing, emotional expression.

1. INTRODUCTION

An Expressive Intelligent Agent (EIA) is a lifelike v irtual human capable o f carrying on conversations with

humans by both understanding and producing speech, hand gesture and emotional facial expressions. Typically

they appear online as a personalized service, navigation guides, and online assistants. Intelligent agent may be

used as a chatting partner in conversation scenarios that will makes the conversation more interesting and

enjoyable for older people. Many people experience loneliness and depression in older age, either as a result of

liv ing alone or due to lack of close family ties and reduced connections with their cu lture of o rig in, which results

in an inability to act ively part icipate in the community activit ies [1]. In order to overcome loneliness almost half

of the older people expend their leisure time by watching the television as their main form of company. Many of

them are expending time with domesticated pets. Pets commonly provide their owners with benefits, such as

providing companionship to elderly adults who do not have adequate social interaction with other people. The

most popular pets are likely dogs and cats. But there are some problems too. Take care of pets become more

difficult for aged people. Different types of difficulties and health problems go with loneliness. Several studies

have presented that a lack of social support can have unpleasant effects on the health and well-being of older

adults [2, 3], and older adults who face extreme isolation face significantly higher risks of mortality than their

connected peers [4]. A recent meta-analysis estimates that 7-17% of older adults face social isolation and 40%

experience loneliness [5].

This phenomenon motivates most- why we should not provide an intelligent virtual agent in older people’s

homes to reduce their loneliness and make them happy. This agent can be acts as a virtual friend of o lder people.

It can be a friend or a company of their daily life. There is growing evidence that expressive displays can impact

people’s emotions [6]. A virtual agent can provide social support and wellness coaching to isolated older adults,

in their homes, for months or years. As the companion agent will be always on, always availab le, it can provide a

range of support interactions including: companionship dialogue, ga me co-play, exercise and wellness

promotion, social activ ity tracking and promotion facilitat ing connections with family and friends, and memory

improvement tasks, among others. Therefore, a v irtual agent can play a vital ro le to reduce human loneliness and

sadness to keep them happy.

It is quite challenging to make an older people familiar with the technology which can support them

mentally. If assistive technology does not meet the individual needs and preferences of the person it may be

ineffective or may even cause additional confusion or distress. Although there are lots of issues remained

unsolved related to the expressive entertaining agent. In the work, we will propose an expressive agent that can

be used in a chatting scenario. When a person communicates with the other human via online messaging or

chatting, this agent can display emotional expression to his/her partner against their text input. The propose agent

can interacts with the elder people expressively and may help to reduce their loneliness . Psychological evidences

23

P-ID 80 International Conference on Physics Sustainable Development & Technology (ICPSDT-2015)(August 19-20, 2015)

Department of Physics, CUET.

revealed that virtual agent can reduced the loneliness, fell them better and creates feelings of enjoyment [ 7]. It is

quite challenging task in human computer interaction (HCI), to design such an intelligent agent that can display

some expressive or emotive behaviors.

2. RELATED WORK

Technologies designed specifically to provide companionship for o lder people are an emerging area of research.

There are lots of intelligent agents that can display some emotional expressions and used as the assistive

technology. These agents can be physical (i.e, robot) and virtual (i.e., avater). Leite et al. [8] developed a robotic

companion designed for game co-play. Wada et al. [9] have examined non-conversational therapeutic robots, and

Klamer, et al. [10] have examined the health benefits of in-home robots. Faces of robots require different ways to

embody their expressions [1] and various facial robot systems are developed. EveR-4 H33 shows the appropriate

emotions for facial expressions of the android head system, which is a head system with 33 degrees of freedom

[11]. To find the appropriate emot ions for facial expressions of EveR-4 H33, it makes some preliminary

evaluations of facial expressions by applying some theories of basic emotions. The main limitation of EveR-4

H33 is people recognized surprise and sadness, and did not recognize disgust and fear well, by comparison with

the average. Wearable and stationary devices that promote multimedia sharing with family and friends have also

been designed to improve the social-connectedness of isolated adults [12]. Other works focused on the

expression of emotions solely through animat ion [13, 14, 15, 16]. These agents have expressed only six

expressions and they do not have any lower eyelids, which caus ed some difficulty in some of the expressions.

Virtual agents have a human appearance and have to response appropriately which may consists of

expression or information. A preliminary work which describes the ro le of the face and human emotion d isplays

in communication and review prev ious research on emotional display in avatar environments [17]. They design

an approach for emot ion-extract ion from text -only input and some results from in formal user studies with the

system. This avatar will reduce the need for users to switch between typing messages and controlling their avatar

representation. As a consequence of this, the user will be able to maintain more natural communication with the

other people in the collaborative v irtual environment. Another agent known as virtual messenger was developed

to communicate with humans and display facial expression [18]. Kramer et. al. [19] explored design issues of

the conversational virtual human as a socially acceptable autonomous assistive system for elderly and

cognitively impaired users. When designing agents to promote social connectedness, intelligent agents should

focus on autonomous and conversational capabilities. Moreover, the agents should adapt to the changing nature

of the socio-emotional relat ionship users with them. In our work, we have designed an agent that can be able to

make conversation with the users with various expressive capabilities.

3. PROPOSED SYSTEM ARCHITECTURE

The main object ive of our work is to develop a software agent that can be able to express emotions during

human-human interaction scenarios such as when people are communicating with each other through live

messaging. For this purpose, we have developed a framework. Fig. 1 shows an abstract view of this framework.

In the framework, we assume that a user A gives input via text entry and the corresponding expressions against

his/her entry is display to the receiving end user B and vice versa. In our current implementation, we take

expressive text as an input and make animat ion of corresponding to this input. This set up is consists of several

hardware devices such as, keyboard, display device, graphics card, general purpose computer, and so on. In

addition to that to develop the system we need to implement of several software modules. Fig. 2 il lustrates the

schematic representation of these modules with their informat ion flows.

Fig. 1: Abstract view of the proposed framework.

Fig.2 Schemat ic diagram of proposed modules.

data

input input

user A user B

Rule

Generator

Lexical

Analyzer

Expressive

Input Sentence

Parser

Expression

Generator

Parse Tree

Analytical

Output Generator

Lexicon

Token

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3.1 Expressive Input Sentence Expressive sentence is any kind of sentence which can express the feelings of one’s mind. In ou r work, we deal

with the simple sentences only. An expressive sentence represents the emotional status of human mind. For

example, “Today I am happy”.

3.2 Lexical Analyzer Lexical Analyzer is a program module that accepts a sentence as input and produces or separates the individual

words usually called token [20]. Tokens are stored in a list for farther process. As for example the lexical

analyzer output of “Today I am happy” is “Today”, “I”, “am” and “happy”.

3.3 Lexicon A Lexicon is a resource which has an associated software environment database which permits access to its

contents. The database may be custom-designed for the lexical information or a general-purpose database into

which lexical information has been entered [20]. If the structure of the sentence is not matched according to

parse then it will generate an error expression and no tree will be generated. In our project we create a database

having attributed of all the parts of speech. The input of the lexicon is the token and the output is the tagging

words. After matching the tokens we can find

Today Adverb; I Pronoun; am Verb; happy Adjective

3.4 Parser The goal of syntactic analysis or parsing is to determine whether the text string on input is a sentence in the

given language. The result of the analysis contains a description of the syntactic structure of the sentence. In this

paper, we considered the context-free grammar (CFG) [21]. A fragment of CFG for simple sentences is

illustrated in table 1. For example, the syntax or the ru les used to pars e the sentence “Today I am happy” is S

NP VP; NP Adv P; VP V Adj.

Table-1. A s mall fragment of CFG for simple sentences.

3.5 Rule Generator The grammar or rule generator consists of rules of what are legal input streams to the parser [22]. The grammar

is described in a simple language. In our work, we keep some predefined rules which are u sed so frequently to

represent simple sentence. For example, Noun+ Verb+Noun+Adjective, Noun+ Verb+Adjective, Noun+

Verb+Noun+Adjective+ Adverb, and so on.

3.6 Parse Tree Generation A parse tree is a tree that represents the syntactic structure of a string according to some formal grammar [22].

First we read CFG and then choose appropriate rules to parse the sentence. For example we input a sentence

“Today I am happy”. The appropriate parse tree for this sentence is shown in Fig. 3.

S

NP VP

Adv P V Adj

Today I am happy

Fig. 3: Parse tree for “Today I am happy.” Fig. 4: Textures for facial expression

No. Rules for Simple Sentence No. Rules for Simple Sentence

01 S NP VP 11 VP AdjP Verb

02 NP Adv P 12 Adj Adj Adv

03 NP Noun 13 AdjP Adj Adv

04 NP Noun Det 14 VP Verb Det

05 NP P 15 AdjP Adj N

06 NP NP Adj 16 Verb am|is|are |fly|read

07 Noun Sagor|Sun|Bird 17 Adv so|very

08 PN He|She|They|His 18 Adj happy|sad

09 VP NP Verb 19 Det a |an|the

10 VP Verb 20 NP N Adj

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http://en.wikipedia.org/wiki/Lexical_resource

http://en.wikipedia.org/wiki/Database

http://www.wikipedia.org/wiki/Tree_%28data_structure%29

http://www.wikipedia.org/wiki/Syntax

http://www.wikipedia.org/wiki/String_%28computer_science%29

http://www.wikipedia.org/wiki/Formal_grammar

3.7 Expression Generation There are many essential parts in human face that play a vital role for facial expression which are illustrated in

Fig. 4. By the manipulation of these textures of face we can generate different expression. The characteristic of

seven basic facial expressions are given bellow. For our work, we have used FaceGen Modeller 3.5 software

[23]. By the help of that software we can create different human face. Actually this is a graphic MPEG-4

standard compatible facial animation engine. It specifies a set of Face Animation Parameters (FAPs), each

corresponding to a particular facial act ion deforming a face model in its neutral state. A particular facial act ion

sequence is generated by deforming the face model, in its neutral state, according to the specified FAP va lues,

indicating the magnitude of the corresponding action, for the corresponding time instant.

Then the model is rendered onto the screen. It is an open source 3D facial animation framework written in C

programming language and a new WebGL implementation. The C framework allows efficient rendering of a 3D

face model in OpenGL-enabled systems. It has a modular design: each module provides one of the several

common facilit ies needed to create a real-time facial animation application. For facial animat ion the 3D data

dynamic coordinates of passive markers are used to create our face articulator model and to drive directly

expressive face. By using these principals we can create seven expressions such as, joy, sad, surprise, contempt,

fear, anger, and disgust respectively. To represent each expression of human face we have used 100 frames. We

make an image stream and pass the stream one by one to create animation. We pass approximately 23 frames to

make one second animation. Fig. 5 shows the some sample frames for joy expressions.

Fig. 5 A fragment of frames fo r generating the expression, joy.

Fig. 5 : A small fragment of sample frames for generating the expression, joy.

3.8 Analytical Output Generation In this module we tried to represent the label of expression graphically. For example the expression label of

sentence “I am happy.” and “I am very happy.” are normally different. The second sentence usually represents

the higher label o f expression than the first sentence. So we classify the basic facial expression in t wo labels:

positive and negative within the range -5 to +5. The expressions labels of different emotive sentences are

illustrated in Table 2.

Table-2: Expression label of simple sentence.

Positive Expression Negative Expression

Input Type Expression Label Input Type Expression Label

Normal +1 Normal -1

So +2 So -2

Very +3 Very -3

Extreme +4 Extreme -4

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4. EXPERIMENTS

In order to evaluate the proposed expressive agent, we have performed two experiments.

4.1 Experiment 1: To Evaluate the Functionality of the Intelligent Agent

In this experiment, we simulate the various functionality of expressive intelligent agent. We compare the

expressions and measure the correctness of the expressive agent against various expressive sentences.

4.1.1 Procedure

To run the agent at first we have to start the .exe file of the proposed software agent. Then the main interface of

the application will appears. Next we have to give some input sentences in the input text area. The input sentence

is separated into a set of tokens and matched with the database. The database is the collection of words

categorized by parts of speech. After the parts of speech tagging the parser pick the appropriate expressive word

and send to the expression generator. According to the expressive word the expression generator will generates

the expressive behaviors into the animation box and corresponding expressive curve will show in the chart area.

4.1.2 Measures

We measure each facial expression with respect to some parameters such as, muscle around the eyes tightened,

checks raised, lip corners raised, and so on.

Measure 1: Expression comparison

At first we generate a character which has some expressive capabilit ies. From this expressive character we

further generate seven basic facial expressions. Behind every expression there are some common criteria. We try

to follow that characteristics and make the following expressions which is illustrated in table 3.

Table-3: Comparison between normal face and expressive face.

Expression Normal Face Expressive Face

Joy

Anger

Contempt

Disgust

Muscle around the

eyes tightened

Cheeks raised

Lip corners raised

diagonally

Eyebrows pulled down

Upper lids pulled up

Lower lids pulled up

Margins of lips rolled in

Eyes neutral

Lip corner pulled up

and back on one side

only

Eyebrows pulled down

Upper lip pulled up

Lips loose

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Fear

Sad

Surprise

Measure 2: Expression evaluation

An input sentence is parsed and produces corresponding expression against each expressive word. Fig. 6 shows a

typical example of generating joy expression for the input the sentence “I am feeling so delight”. The agent first

parses the sentence [shown in the middle of the Fig. 6] and picks the “delight” word as an expressive word .

Based on the expressive word it generates joy expression. It also shows the expression label as a graph [indicates

right hand side of the Fig. 6].

Fig. 6: Output animation of joy expression in the sentence, ‘I am feeling so deligh’.

Form the above figure we can see the joy expression. We have tested other expressions such as anger, fear,

contempt, surprise, disgust and sad respectively. Fig. 7 also illustrates another example for the sad expression in

the sentence, ‘I am so sorry’.

Eyebrows pulled up

and together

Mouth stretched

Inner corners of

eyebrows raised

Eyelids loose

Lip corners pulled down

Ent ire eyebrow pulled up

Eyelids pulled up

Mouth hangs open

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Fig. 7: Output animation of sad expression in the sentence, ‘I am so sorry’.

Measure 3: Accuracy

We tested several simple sentences for p roducing expressions. Most of these sentences are collected from the

English books [24] and newspapers; few are generated by the authors . Table 3 shows some example sentences

with their expression category, expression label, and recognition.

Table-4: Expression type of some simple sentence.

Input Sentences Expression Category Expression Label Remarks

He is feeling delight Joy +1 Correct

It is very irritating Anger -3 Correct

My uncle is dead Sad -1 Correct

He was held in contempt Contempt -1 Correct

You are a naughty boy Contempt -1 Correct

They are so crazy Anger -2 Correct

I got so tired of playing football Disgust -2 Correct

The movie is very horrific Fear -3 Correct

She is extremely surprised Surprise +4 Correct

Wild animal is good Anger -1 Wrong

We have tested about 225 sentences with 50 different expressive words. The length of sentences varies from

3 to 7 words. Out of 50 expressive words, 47 are recognized. Thus, the agent can produce expressions about 94%

of times correct ly against expressive words that have written by the users. In few cases, the system did not

correctly generate the appropriate expressions. For example , in the sentence, ‘wild an imal is good’. The system

recognized that wild as an adjective and generated angry expression which is not correct.

Measure 4: Res ponse time

The total response time is div ided into two parts. One is parse time and another is animation time. The animat ion

time depends on the expression label. The total response time is represented as Fig. 8. As a typical example,

Table 5 shows response time for some input sentences.

Fig. 8 : Response time Scale

The equation of total response time may calculate from the fo llowing equation:

12

0

1

ii

t

ii ttttTi

Parse Time Animat ion Time

(1)

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Here, = Total t ime; =Starting t ime of parsing; = Ending time of parsing/starting time of animat ion;

= Ending time of an imation

Table-5: Response time of some simple sentence

Input Sentences Response Time (sec)

Parse Time (sec) Animation Time (sec)

I am happy 00.0070004 02.8971657

He is feeling delight 00.0130008 02.9851707

They are unhappy 00.0120007 04.5362595

It is very irritating 00.0120007 05.3893060

My uncle is dead 00.0160009 04.5252588

4.2 Experiment 2: To Evaluate the Expressiveness of Intelligent Agent in HCI Scenarios

In this experiment, we measure the users’ impressions regarding the proposed agent while interacting with it. We

recruited 20 participants to evaluate our system of different ages. Two faculty members, two sub assistant

engineers, six housewives, five business personal, two school teachers, and three government employees are

requested to participate in this experiment. They are randomly selected and all of them are resident of Chittagong

division. Mean ages of the participant are 39.45 year (Std = 13.14).

4.2.1 Procedures

After providing the agent to the participants we gave them instructions how to use the agent, its functionality and

the motivation of our project. They interact with the agent in various time periods. The average interacting time

of the participants is about 20 minutes. During this time period they try to share their feelings with the agent.

After interaction, they leave their feedback which is quite satisfactory. Fig. 9 shows an interacting scenario with

the agent.

Fig. 9 : Some snapshots of experiment while the participant interacting with the intelligent agent.

4.2.2 Measurements

We asked participants to fill out a questionnaire after interactions were complete. The measurement was a simple

rating on a Likert scale of 1- to- 7. The questionnaire consists of 5 items which is illustrated in table 5.

Table 5: Questionnaires for subjective evaluation.

Items Questionnaire

Q1 Did you feel intelligence of the agent when interacting with it?

Q2 Did you think that the agent is interesting?

Q3 What did you think that the agent delivered its expression successfully?

Q4 Did the expressions produced by the agent are appropriate?

Q5 Rate your overall impression about the agent?

4.2.3 Results

We conducted the measures of analysis of variance (ANOVA) on participant’s rating. Fig. 10 shows the

result of post questionnaire analysis of subjective evaluation. We observed a total of 100 (20×5) questionnaire

for all part icipants. There are no significant differences were found among questionnaire responses (F (4, 99) =

1.50, p=0.20, 2 = 1.5) of participants. Fig. 10 ind icates the mean and standard error values for questionnaire.

Although, satisfaction depends on the individual characteristics result indicates that participants were quite

satisfied to interacting with the proposed agent. In this work, we try to develop a framework for o lder people that

can create a feeling of virtual friend to them. This agent can display various expressions against the participants’

emotional state that can express by the text.

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Fig. 10: Result of post questionnaire for the subjective evaluation. Error bars indicates the standard errors.

5. CONCLUSION There are many ways to display facial expressions through the intelligent agent. The generation of realistic facial

animation is to reproduce the contextual variab ility due to the reciprocal influence of art iculator movements. It is

extremely complex and difficult to model. Moreover, emotions are quite important in human interpersonal

relations and individual development. Our proposed framework may not express a vast range of expression but

the expression made by the agent is quiet satisfactory. It can provide with a positive feeling when people

interacting with the agent. Thus it has some emot ional benefits. People can express their feelings as well as their

emotions through the agent. So they found a virtual friend as a means of communicat ion. The experime ntal

results including subjective and quantitative experiment shows that the proposed agent is functioning quite well.

The proposed system can be helpful during interacting of humans via live messaging and get some mental

satisfaction for a moment which in turn reduces the loneliness of them. Adding sound corresponding to every

expression and full body animation will be the more effective commutative interface which is left for our future

issues.

REFERENCES [1] H. S. Lee, J. W. Park and M. J. Chung, A Linear Affect–Expression Space Model and Control Points

for Mascot-Type Facial Robots, IEEE Transactions on Robotics, pp. 863-873, 2007.

[2] S. Bassuk, T. Glass, L. Berkman, Social Disengagement and Incident Cognitive Decline in

Community-Dwelling Elderly Persons, Annals of Internal Medicine, vol. 131, no. 3, pp. 165-173,

1999.

[3] L. P. Vardoulakis, L. Ring, B. Barry, C. Sidner, and T. Bickmore, Designing Relational Agents as

Long Term Social Companions for Older Adults, In Proc. Int. Conf. on Intelligent Virtual Agents,

pp. 289-302, Santa Cruz, CA, USA, 2012.

[4] L. Berkman, L. Syme, Social Networks, Host Resistance, and Mortality: A Nine-year Follow-up

Study of Alameda County Residents, American Journal of Epidemiology, vol. 109, no. 2, pp. 186-

204, 1979.

[5] A. Dickens, S. Richards, C. Greaves, J. Campbell, Interventions Targeting Social Isolation in Older

People: A Systematic Review, BMC Public Health, vol. 11, no. 1, pp. 647, 2011.

[6] R. Yaghoubzadeh, M. Kramer, K. Pitsch, S. Kopp, Virtual Agents as Daily Assistants for Elderly or

Cognitively Impaired People, Intelligent Virtual Agents, LNCS, vol. 8108, pp 79-91, 2013.

[7] R. Lazlo, B. Barbara, T. Kathleen, B. Timothy, Addressing Loneliness and Isolation in Older Adults,

Archives of Internal Medicine, vol. 172, pp. 1078-1083.

[8] I. Leite, S. Mascarenhas, A. Pereira, C. Martinho, R. Prada, A. Paiva, Why can't we be friends? An

empathic game companion for long-term interaction, In Proc. 10th International Conference on

Intelligent Virtual Agents, Philadelph ia, PA, Springer-Verlag, pp 315-321, 2010.

[9] S. Wada, T. Shibata, Robot Therapy in a Care House - Change of Relationship among the Residents

and Seal Robot during a 2-month Long Study, In Proc. 16th IEEE International Symposium on

Robot and Human interactive Communication, pp. 107-112, 2007.

[10] T. Klamer, S. B. Allouch, Acceptance and Use of a Social Robot by Elderly Users in a Domestic

Environment, Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010.

Page 131



[11] H. S Ahn, D. W. Lee, D. Choi, D. Lee, M. Hur and H. Lee, Appropriate Emotions for Facial

Expressions of 33-DOFs Android Head EveR-4 H33 Advanced Telecommunications Research

Institute, pp. 1115-1120, 2012.

[12] Chen C-Y, Kobayashi M, Oh L ShareCom p : sharing for companionship. In: CHI '05 extended

abstracts on Human factors in computing systems, Portland, OR, USA, 2005. ACM, pp 2074 -2078.

[13] C. Bartneck, Affective expression of machines. In CHI’ 01 Extended Abstracts on Human Factors

in Computing Systems, pp. 255-260. New York, USA, 2001.

[14] C. Breazeal and P. Fitzpatrick, Social implication of animate vision. In Proc. AAAI Fall

Symposium, Socially Intelligent Agents -The Human in the Loop, 2000.

[15] C. Breaseal, and R. Brooks, Robot emotion: A functional perspective. Who needs emotions: the

brain meets the robot, Oxford University Press, 2004.

[16] U. Hes, The communication of emotion. Emotions, Qualia, and Consciousness, Singapore, 2001.

[17] T. Ribeiro, A. Paiva, The Illusion of Robotic Life: Principles and Practices of Animation for Robots

March 8, 2012, Boston, Massachusetts, USA.

[18] Intelligent Expressive Avater, Available: http://www.hit l.washington.edu/r-98-32/ [Online].

[19] K. Kramer, R. Yaghoubzadeh, S. Kopp, K. Pitsch, A conversational virtual human as autonomous

assistant for elderly and cognitively impaired users? Social acceptability and design consideration ,

LNI, pp. 1105-1119, 2013.

[20] L. Riccardo, C. Peiro, A Facial Animation Framework with Emotive/Expressive Capabilities, In

IADIS International Conference Interfaces and Human Computer Interaction, pp. 49-53, Italy, 2011.

[21] A. Tru jillo, Translation Engines: Techniques for Machine Translation , (Springer-verlag, London,

1992).

[22] D. Arnold, L. Balkan, S. Meijer, R. L. Humphreys, and L. Sad ler, Machine Translation An

Introductory Guide, (Ncc, Blackwell Ltd., London, 1994).

[23] MakeHuman, http://www.makehuman.org/.

[24] S. Greenbaum, The Oxford English Grammar , Clarendon Press 1996.

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