AIML Based Voice Enabled Artificial Intelligent Chatterbot
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AIML Based Voice Enabled Artificial Intelligent Chatterbot
Article · February 2015
DOI: 10.14257/ijunesst.2015.8.2.36
CITATIONS
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2 authors, including:
Imran Ahmed
VIT University
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AIML Based Voice Enabled Artificial Intelligent Chatterbot
Article · February 2015
DOI: 10.14257/ijunesst.2015.8.2.36
CITATIONS
8
READS
1,578
2 authors, including:
Imran Ahmed
VIT University
6 PUBLICATIONS 14 CITATIONS
SEE PROFILE
All content following this page was uploaded by Imran Ahmed on 17 March 2015.
The user has requested enhancement of the downloaded file.
International Journal of u- and e- Service, Science and Technology
Vol.8, No.2 (2015), pp.375-384
http://dx.doi.org/10.14257/ijunesst.2015.8.2.36
ISSN: 2005-4246 IJUNESST
Copyright ⓒ 2015 SERSC
AIML Based Voice Enabled Artificial Intelligent Chatterbot
Imran Ahmed and Shikha Singh
University Institute of Engineering and Technology,
CSJM University, Kanpur, Uttar Pradesh
imranahmed2k9@gmail.com, sshikha462@gmail.com
Abstract
This paper shows the implementation of an artificial intelligent chatterbot with whom
human can interact by speaking to it and receive a response by chatterbot using its speech
synthesizer. Objective of this paper is to show application of chatterbot that can be used
in various fields like education, healthcare, and route assistance. It is statistical model
and chatterbot is based on AIML (Artificial Intelligent Markup Language) structure for
training the model and uses Microsoft voice synthesizer for providing speech recognition
system and natural language processing.
Keywords: chatterbot, speech recognition, AIML, intelligent system, natural language
processing
1. Introduction
By using natural language user can interact with system very easily even if user doesn’t
know how to read and write so, it becomes incentive for user to use this system. The
model receives response from user either in form of text or in form of speech and thus
responds to user in both format text as well as sound thus facilitating simple and user
friendly request and response system. Developing such system raises many issues as
accent varies from person to person resulting in understanding what is meaning of user
request. The performance also varies according to amount of corpus available for training
the model.
Concept of chatterbot came into existence with “Alice” which is used to receive
question from user and it was based on pattern recognition. Large amount of sentences
were involved in training the model. The response provided by this bot was very
monotonous which gives feeling of chatterbot being a machine and it’s functionality were
very limited as response for each input was predefined.
Chatterbot is sub field of AI. AI is artificial intelligence which is a branch of computer
science whose main focus is to develop and study intelligent programs and machine
which can be helpful to people in their daily work and make their life easy. Artificial
intelligence is not limited to chatterbot it involves NLP (natural language processing),
Social aware intelligence, knowledge representation and inferring from that knowledge.
This is bot system implemented by using AIML. AIML is artificial intelligence markup
language which is another flavor of XML (extensible markup language). AIML has been
used in this model such that it parsing and declaration different kinds of sentences can be
easy. AIML is base of chatter bot brain.
For the purpose of natural language understanding, Microsoft speech recognition is
used which helps in speech recognition and speech synthesis for speech to text and text to
speech to make it more user friendly for people.
Vol.8, No.2 (2015), pp.375-384
http://dx.doi.org/10.14257/ijunesst.2015.8.2.36
ISSN: 2005-4246 IJUNESST
Copyright ⓒ 2015 SERSC
AIML Based Voice Enabled Artificial Intelligent Chatterbot
Imran Ahmed and Shikha Singh
University Institute of Engineering and Technology,
CSJM University, Kanpur, Uttar Pradesh
imranahmed2k9@gmail.com, sshikha462@gmail.com
Abstract
This paper shows the implementation of an artificial intelligent chatterbot with whom
human can interact by speaking to it and receive a response by chatterbot using its speech
synthesizer. Objective of this paper is to show application of chatterbot that can be used
in various fields like education, healthcare, and route assistance. It is statistical model
and chatterbot is based on AIML (Artificial Intelligent Markup Language) structure for
training the model and uses Microsoft voice synthesizer for providing speech recognition
system and natural language processing.
Keywords: chatterbot, speech recognition, AIML, intelligent system, natural language
processing
1. Introduction
By using natural language user can interact with system very easily even if user doesn’t
know how to read and write so, it becomes incentive for user to use this system. The
model receives response from user either in form of text or in form of speech and thus
responds to user in both format text as well as sound thus facilitating simple and user
friendly request and response system. Developing such system raises many issues as
accent varies from person to person resulting in understanding what is meaning of user
request. The performance also varies according to amount of corpus available for training
the model.
Concept of chatterbot came into existence with “Alice” which is used to receive
question from user and it was based on pattern recognition. Large amount of sentences
were involved in training the model. The response provided by this bot was very
monotonous which gives feeling of chatterbot being a machine and it’s functionality were
very limited as response for each input was predefined.
Chatterbot is sub field of AI. AI is artificial intelligence which is a branch of computer
science whose main focus is to develop and study intelligent programs and machine
which can be helpful to people in their daily work and make their life easy. Artificial
intelligence is not limited to chatterbot it involves NLP (natural language processing),
Social aware intelligence, knowledge representation and inferring from that knowledge.
This is bot system implemented by using AIML. AIML is artificial intelligence markup
language which is another flavor of XML (extensible markup language). AIML has been
used in this model such that it parsing and declaration different kinds of sentences can be
easy. AIML is base of chatter bot brain.
For the purpose of natural language understanding, Microsoft speech recognition is
used which helps in speech recognition and speech synthesis for speech to text and text to
speech to make it more user friendly for people.
International Journal of u- and e- Service, Science and Technology
Vol.8, No.2 (2015)
376 Copyright ⓒ 2015 SERSC
2. Related Works
The bot [1, 2] system began in early nineteenth century where first chess playing
machine was built such that a system act as a player and other player an human can play
against it, such that system giving a feeling of another person existence. Here is different
chatter bot build for various purposes.
ALICE [7-9] was implemented with pattern matching algorithm which was as simple
as string matching technique. ALICE takes the text as input and produces output as text
which was like question and answer based system. Whereas another chatter bot[5][6]
which was build earlier known as Elizabeth requires set of input rules, keyword patterns
for identifying input and output rules to produce required response.
The recursion [3, 4] based approach was implemented in ALICE chatter bot and it is a
vital feature for the model to work properly. Since it was recursion based so calling
function by itself made it easy to respond appropriately but it overflow the stack.
However rules in other bot may lead to language specific chatter [23-25] bot such that it
sticks to particular language therefore to make it language independent one has revised all
the rules to work it properly and produce output.
ALICE can combine two answers in the case of splitting during the normalization
process, or by recursive process. In Elizabeth [10-12] we cannot partition the sentence in
two parts and then combine the result.
Pattern-managing matching process is the vital feature of ALICE chatter bot, but it is
simple and used algorithm is depth-first search which result in producing no output also.
DFS [15, 17, 18] tries to find algorithm finds the longest common subsequence pattern
matching the required text in the database where other one Elizabeth produce output
according to the first keyword [21, 22] matched.
Both systems are memory based. Elizabeth's dynamic process, it can perform other
actions while other conversation is in process, is considered a excellent feature which is
not in ALICE.
If the inputs are repeated during the conversation, Elizabeth gives different answers by
use of different random selection responses from the list. ALICE use random selection
which can lead to duplicate responses.
Since Elizabeth is based on specific language [13, 14, 16], therefore rules written for it
also language based, therefore it is difficult to other language user to interact. However
language independent was ALCIE but it was string matching based which was not
fruitful.
Elizabeth requires large amount of analysis tables for the steps to perform in matching
[19, 20] a text or string, therefore for the developer how answer question is carried out.
From the above it can be understood that none of the previous bot was perfect such that it
raises a need of new chatter bot which is language independent and it does not entitle to
input a text by writing it has to produce output by just voice enabled and user doesn’t has
to read also it should also be voice response such that it becomes user friendly for user
and it ease of use should it uniqueness.
3. Proposed Work
The architecture of bot system is divided into following modules which helped in
implementing this chatterbot.
i) AIML
ii) Microsoft Speech recognition
iii) Corpus
iv) Brain
v) Bot engine
Vol.8, No.2 (2015)
376 Copyright ⓒ 2015 SERSC
2. Related Works
The bot [1, 2] system began in early nineteenth century where first chess playing
machine was built such that a system act as a player and other player an human can play
against it, such that system giving a feeling of another person existence. Here is different
chatter bot build for various purposes.
ALICE [7-9] was implemented with pattern matching algorithm which was as simple
as string matching technique. ALICE takes the text as input and produces output as text
which was like question and answer based system. Whereas another chatter bot[5][6]
which was build earlier known as Elizabeth requires set of input rules, keyword patterns
for identifying input and output rules to produce required response.
The recursion [3, 4] based approach was implemented in ALICE chatter bot and it is a
vital feature for the model to work properly. Since it was recursion based so calling
function by itself made it easy to respond appropriately but it overflow the stack.
However rules in other bot may lead to language specific chatter [23-25] bot such that it
sticks to particular language therefore to make it language independent one has revised all
the rules to work it properly and produce output.
ALICE can combine two answers in the case of splitting during the normalization
process, or by recursive process. In Elizabeth [10-12] we cannot partition the sentence in
two parts and then combine the result.
Pattern-managing matching process is the vital feature of ALICE chatter bot, but it is
simple and used algorithm is depth-first search which result in producing no output also.
DFS [15, 17, 18] tries to find algorithm finds the longest common subsequence pattern
matching the required text in the database where other one Elizabeth produce output
according to the first keyword [21, 22] matched.
Both systems are memory based. Elizabeth's dynamic process, it can perform other
actions while other conversation is in process, is considered a excellent feature which is
not in ALICE.
If the inputs are repeated during the conversation, Elizabeth gives different answers by
use of different random selection responses from the list. ALICE use random selection
which can lead to duplicate responses.
Since Elizabeth is based on specific language [13, 14, 16], therefore rules written for it
also language based, therefore it is difficult to other language user to interact. However
language independent was ALCIE but it was string matching based which was not
fruitful.
Elizabeth requires large amount of analysis tables for the steps to perform in matching
[19, 20] a text or string, therefore for the developer how answer question is carried out.
From the above it can be understood that none of the previous bot was perfect such that it
raises a need of new chatter bot which is language independent and it does not entitle to
input a text by writing it has to produce output by just voice enabled and user doesn’t has
to read also it should also be voice response such that it becomes user friendly for user
and it ease of use should it uniqueness.
3. Proposed Work
The architecture of bot system is divided into following modules which helped in
implementing this chatterbot.
i) AIML
ii) Microsoft Speech recognition
iii) Corpus
iv) Brain
v) Bot engine
International Journal of u- and e- Service, Science and Technology
Vol.8, No.2 (2015)
Copyright ⓒ 2015 SERSC 377
i). AIML
Artificial intelligence markup language is a flavor of XML, extensible markup
language and it is custom form XML. The most important parts of an AIML documents
are:
a) <aiml>
b) <category>
c) <pattern>
d) <template>
There are many others tags which are used to describe a scene for bot to understand
and respond appropriately. It’s main objective is to simplify pattern matching, request
response from knowledge to be carried out and as well as to exist in the web development
where information can be sent via HTTP. AIML consists of aiml objects and these aiml
objects consist of topics and categories which contain either parsed or unparsed data from
their information is extracted. Data which is parsed consists of characters and this
character data is being parsed by the aiml parser or interpreter. An Aiml interpreter can be
describe as a one who scan aiml objects and provide a response according to that
characters. A parser is biggest part of application like bot program. There are some goals
for design that need to be existed for betterment.
1) It should be simple and easy to learn for people and machine.
2) With minimal encoding and decoding should provide precise result.
3) A simple program can be used to parse aiml documents.
4) There should not be any dependencies for particular language.
AIML Objects
AIML Root
It is the root element from where there is initiation of aiml document. It can be
compared with html document where writing begins after specifying its html file same is
the case here
<aiml>....</aiml>. So it also have opening and closing tag also. AIML
TOPIC
It is first level optional item that specify category of elements. It has following attributes
namely ‘name’.
<aiml:topic name=’topic name’>...</aiml:topic>
AIML Category
It is first level if topic not present else it is second level element that contains only one
pattern and exactly only one template. It doesn’t contain any attribute.
<aiml:category>
......Content for category...
</aiml:category>
AIML Pattern
Vol.8, No.2 (2015)
Copyright ⓒ 2015 SERSC 377
i). AIML
Artificial intelligence markup language is a flavor of XML, extensible markup
language and it is custom form XML. The most important parts of an AIML documents
are:
a) <aiml>
b) <category>
c) <pattern>
d) <template>
There are many others tags which are used to describe a scene for bot to understand
and respond appropriately. It’s main objective is to simplify pattern matching, request
response from knowledge to be carried out and as well as to exist in the web development
where information can be sent via HTTP. AIML consists of aiml objects and these aiml
objects consist of topics and categories which contain either parsed or unparsed data from
their information is extracted. Data which is parsed consists of characters and this
character data is being parsed by the aiml parser or interpreter. An Aiml interpreter can be
describe as a one who scan aiml objects and provide a response according to that
characters. A parser is biggest part of application like bot program. There are some goals
for design that need to be existed for betterment.
1) It should be simple and easy to learn for people and machine.
2) With minimal encoding and decoding should provide precise result.
3) A simple program can be used to parse aiml documents.
4) There should not be any dependencies for particular language.
AIML Objects
AIML Root
It is the root element from where there is initiation of aiml document. It can be
compared with html document where writing begins after specifying its html file same is
the case here
<aiml>....</aiml>. So it also have opening and closing tag also. AIML
TOPIC
It is first level optional item that specify category of elements. It has following attributes
namely ‘name’.
<aiml:topic name=’topic name’>...</aiml:topic>
AIML Category
It is first level if topic not present else it is second level element that contains only one
pattern and exactly only one template. It doesn’t contain any attribute.
<aiml:category>
......Content for category...
</aiml:category>
AIML Pattern
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