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Survey on Chatbot Design Techniques in Speech Conversation Systems (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 7, 2015 Survey on Chatbot Design Techniques in Speech Conversation Systems Sameera A. Abdul-Kader Dr. John Woods School of Computer Science and Electronic School of Computer Science and Electronic Engineering/University of Essex Colchester/ UK Engineering/University of Essex Colchester/ UK Diyala University/ Diyala/ Iraq Abstract—Human-Computer Speech is gaining momentum as selected works are compared with those used in Loebner-Prize a technique of computer interaction. There has been a recent Chatbots. The findings are discussed and conclusions are upsurge in speech based search engines and assistants such as drawn at the end. Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to II. BACKGROUND analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like A. Human-Computer Speech interaction responses. This type of programme is called a Chatbot, which is Speech recognition is one of the most natural and sought the focus of this study. This paper presents a survey on the after techniques in computer and networked device interaction techniques used to design Chatbots and a comparison is made has only recently become possible (last two decades) with the between different design techniques from nine carefully selected advent of fast computing. papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the Speech is a sophisticated signal and happens at different last decade. The paper discusses the similarities and differences levels: “semantic, linguistic, articulatory, and acoustic” [3]. in the techniques and examines in particular the Loebner prize- Speech is considered as the most natural among the aspects of winning Chatbots. human communication, owing to copious information implicitly existing beyond the meaning of the spoken words. Keywords—AIML; Chatbot; Loebner Prize; NLP; NLTK; SQL; One of the speech information extraction stages is converting Turing Test speech to text via Automatic Speech Recognition (ASR) and mining speech information [4]; then, the resulting text can be I. INTRODUCTION treated to extract the meaning of the words. Speech is one of the most powerful forms of communication between humans; hence, it is the researchers‟ Speech recognition is widely accepted as the future of ambition in the human computer interaction research field to interaction with computers and mobile applications; there is improve speech interaction between the human and the no need to use traditional input devices such as the mouse, computer in order to simulate human-human speech keyboard or touch sensitive screen and is especially useful for interaction. Speech interaction with modern networked users who do not have the ability to use these traditional computing devices has received increasing interest in the past devices [5]. It can help disabled people with paralysis, for few years with contributions from Google, Android and IOS. example, to interact with modern devices easily by voice only Because they are more natural than graphic-based interfaces, without moving their hands. spoken dialogue systems are beginning to form the primary B. Natural Language Toolkit (NLTK) interaction method with a machine [1]. Therefore, speech In order to deal with and manipulate the text resulting from interaction will play a significant role in humanising machines speech recognition and speech to text conversion, specific in the near future [2]. toolkits are needed to organise the text into sentences then Much research work has focussed on improving split them into words, to facilitate semantic and meaning recognition rates of the human voice and the technology is extraction. One of these toolkits is the widely used NLTK now approaching viability for speech based human computer which is a free plugin for Python. interaction. Speech Interaction splits into more than one area The Natural Language ToolKit (NLTK) is a set of including: speech recognition, speech parsing, NLP (Natural modules, tutorials and exercises which are open source and Language Processing), keyword identification, Chabot cover Natural Language Processing symbolically and design/personality, artificial intelligence etc. Chatbot is a statistically. NLTK was developed at the University of computer program that have the ability to hold a conversation Pennsylvania in 2001allowing computational linguistics with with human using Natural Language Speech. three educational applications in mind: projects, assignments In this paper, a survey of Chatbot design techniques in and demonstrations [6] [7]. It can be found within the Python speech conversation between the human and the computer is Libraries for Graph manipulation GPL open license. NLTK is presented. Nine studies that made identifiable contributions in used to split words in a string of text and separate the text into Chatbot design in the last ten years are selected and then, parts of speech by tagging word labels according to their reviewed. The different techniques used for Chatbots in the positions and functions in the sentence. The resulting tagged 72 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 7, 2015 words are then processed to extract the meaning and produce a response as speech or action as required. Different grammar rules are used to categorise the tagged words in the text into groups or phrases relating to their neighbours and positions. This type of grouping is called chunking into phrases, such as noun phrases and verb phrases. C. Chatbot strategies To give suitable answers to keywords or phrases extracted from speech and to keep conversation continuous, there is a need to build a dialogue system (programme) called a Chatbot (Chatter-Bot). Chatbots can assist in human computer interaction and they have the ability to examine and influence the behaviour of the user [8] by asking questions and Fig. 1. Components of Chatbot [11] responding to the user's questions. The Chatbot is a computer programme that mimics intelligent conversation. The input to D. Chatbot Fundamental Design Techniques and approaches this programme is natural language text, and the application To design any Chatbot, the designer must be familiar with should give an answer that is the best intelligent response to a number of techniques: the input sentence. This process is repeated as the conversation continues [9] and the response is either text or 1) Parsing: this technique includes analysing the input speech. text and manipulating it by using a number of NLP functions; Building a Chatbot needs highly professional for example, trees in Python NLTK. programming skills and experienced developers to achieve 2) Pattern matching: it is the technique that is used in even a basic level of realism. There is a complicated most Chatbots and it is quite common in question-answer development platform behind any Chatbot which will only be systems depending on matching types, such as natural as good as its knowledge base which maps a user‟s words into language enquiries, simple statements, or semantic meaning the most appropriate response. The bot developer usually of enquiries [12]. builds the knowledge base as well. However, there are some 3) AIML: it is one of the core techniques that are used in platforms which provide a learning environment. Writing a common Chatbot design. More details about this technique perfect Chatbot is very difficult because it needs a very large and the language used are explained in section 2.5 below. database and must give reasonable answers to all interactions. 4) Chat Script: is the technique that helps when no There are a number of approaches to create a knowledge base for a Chatbot and include writing by hand and learning from a matches occur in AIML. It concentrates on the best syntax to corpus. Learning here means saving new phrases and then build a sensible default answer. It gives a set of functionalities using them later to give appropriate answers for similar such as variable concepts, facts, and logical and/or. phrases [10]. 5) SQL and relational database: is a technique used recently in Chatbot design in order to make the Chatbot Designing a Chatbot software package requires the identification of the constituent parts. A Chatbot can be remember previous conversations. More details and divided into three parts: Responder, Classifier and explanation are provided in section 2.6 below. Graphmaster (as shown in Figure. 1) [11], which are described 6) Markov Chain: is used in Chatbots to build responses as follows: that are more applicable probabilistically and, consequently, are more correct. The idea of Markov Chains is that there is a 1) Responder: it is the part that plays the interfacing role fixed probability of occurrences for each letter or word in the between the bot’s main routines and the user. The tasks of the same textual data set [13]. responder are: transferring the data from the user to the 7) Language tricks: these are sentences, phrases, or even Classifier and controlling the input and output. paragraphs available in Chatbots in order to add variety to 2) Classifier: it is the part between the Responder and the the knowledge base and make it more convincing. The types of Graphmaster. This layer’s functions are: filtering and language tricks are: normalising the input, segmenting the input entered by the Canned responses. user into logical components, transferring the normalised sentence into the Graphmaster, processing the output from the Typing errors and simulating key strokes. Graphmaster, and handling the instructions of the database Model of personal history. syntax (e.g. AIML). 3) Graphmaster: is the part for pattern matching that Non Sequitur (not a logical conclusion) does the following tasks: organising the brain’s contents, Each of these language tricks is used to satisfy a specific storage and holding the pattern matching algorithms.
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