International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN

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1 Nikita Goyal 1, Umang Bhate 2, Varun Punamiya 3, Aditi Gagrani 4, Venkatesan N. 5 1 Department of Computer Engineering, MIT College Of Engineering, Pune, Maharashtra 2 Department of Computer Engineering, MIT College Of Engineering, Pune, Maharashtra 3 Department of Information Technology, MIT College Of Engineering, Pune, Maharashtra 4 Department of Information Technology, MIT College Of Engineering, Pune, Maharashtra 5 Department of Computer Engineering, MIT College Of Engineering, Pune, Maharashtra ABSTRACT: A Smart Personal Assistant is a mobile software system that has the ability to perform tasks, or services, on behalf of an individual user, based on a combination of user input, location awareness, and the ability to access information from a variety of online sources. We propose a system which uses Hindi as the language of choice for the assistant and plan to extend the scope by adding other Indian languages. The system uses speech recognition and machine learning model to perform the requested action given by the user in Hindi. The huge success of smart personal assistants is overwhelming and well accepted by people all across the globe. Its advantages are felt in all wakes of life. However language proves to be a significant barrier in its adoption since most of the assistants are language and region specific such as Siri for IOS, Cortana for Windows and Google Now for Android all support English language only. Keywords: Voice Assistant, Hindi-language, SVM, Classifier, Dialogue Manager, Android System, Naive-Bayes [1] INTRODUCTION A virtual assistant is a software agent that can perform tasks or services for an individual. The popularity of virtual assistants has increased to a great extent as developing systems with integration of such assistants has become a trend in today s technological world. Several efforts have been made towards creating personal assistant software (PAS) to Nikita Goyal, Umang Bhate, Varun Punamiya, Aditi Gagrani, Venkatesan N. 1

2 help people in their daily life activities, at home or at work. Google Now for Android based smart phones, Apple Inc s Siri for ios systems, Cortana for Windows and Alexa by Amazon are some examples to name the same. These systems perform tasks implicitly just by interpreting user s voice commands given in English language. The tasks include miscellaneous actions like calling a contact, messaging, opening an application present in the system, setting an alarm, web searching for queries and many more. Support for local languages in these systems is limited to web search only. They are unable to interpret commands in other languages except English creating a huge gap between people comfortable and uncomfortable in English language. Thus the need to develop systems supporting local languages for assisting users is felt. This project aims at developing personal assistant for Android based smart phones supporting Hindi, the major language being spoken in India. Through this project we intend to propose a model that can be extended to support many prominent local languages across the globe. To eliminate language barrier in usage of virtual assistants serve as prime motivation for the project. The existing system Vaani [1] had certain drawbacks due to which it cannot be implemented on full scale. It used text matching and linear searching to select between the different functionalities (e.g. call, messaging, etc).this proved to be time consuming where multiple synonyms exist for the word describing the functionalities. Machine learning improves the accuracy of selection of multiple functionalities. The classification techniques for textual data were compared in [2]. It clearly states and proves why SVM is a better choice over Naive-Bayes. The problem faced in classifying textual data which includes proper nouns is solved using SVM. Tests prove that SVM clearly outperforms Naive-Bayes in case of textual data. [2] PROPOSED SOLUTION The application takes Hindi speech given by the user as input. For example, "Umang ko call karo", "Aditi ko message karo", "kripya camera kholde" and many more similar sentences in Hindi language. The aim is to identify the object, action and additional parameters associated with the input so that appropriate functionality is performed. The input is in the form of audio signal which gets passed by the application to an instance of RecognizerService class of android.speech package developed by Google. The corresponding speech to text data provided by the RecognizerService is displayed in a text view mode by the application to the user. The text is then further passed to the remote server where appropriate classifier is present for the analysis and processing of the text. Almost all the known techniques for classification such as decision trees, decision rules, Bayes methods, nearest neighbor classifiers, SVM classifiers, and neural networks have been extended to the case of text data. Recently, a considerable amount of emphasis has been placed on linear classifiers such as neural networks and SVM classifiers, with the latter being particularly suited to the characteristics of text data[2]. Hence, we are proposing to use SVM Classifier, for text classification, required at the server side of the system. It is a SVM model which is pre-trained with sample inputs and parameters which is then used to classify the new input given to it. It takes speech to text data from the application and analyses it so as to classify it into corresponding classes of actions such as call, message, etc relative to the user s command given for that instance. Nikita Goyal, Umang Bhate, Varun Punamiya, Aditi Gagrani, Venkatesan N 2

3 As soon as the action in relation with the given command by the user is identified by the classifier the Dialogue Manager in that class does the work of extracting important parameters and objects required for the fulfillment of the action. The relative action and parameters associated with it is passed to the application in order to perform the user s given task. According to the action that needs to be triggered, appropriate intents encapsulated with corresponding data are issued by the android application to the android system. Figure: 1. Functional block diagram [3] ACTUAL FLOW OF CONTROL The proposed system consists of a client-server model where the Android App acts as the client and the server runs the classifying algorithm. 1. The input to the app is given in the form of voice input in Hindi Language. The voice input is converted into English text. The use of English text makes it faster and easier for the App to find the contact names as majority of the names in contact list are stored using the English language i.e. a Hindi input representing "उम ग क क ल कर " is represented as Umang ko call karo by the app. This is achieved using Google s available Speech to Text API. 2. This text is now sent to the server for classifying to determine the appropriate action to be performed. It is sent in JSON format since JSON objects can be handled well by both Python and Java. 3. At the server, major preprocessing tasks are carried out before the actual classification. The input is scanned to find negative words if any, by comparing it with a text file which consists of negative words by using the Bag of Words Model. If present, the action is set to some default command predefined. The commonly found Nikita Goyal, Umang Bhate, Varun Punamiya, Aditi Gagrani, Venkatesan N. 3

4 negative words are mat, nahi, na etc. The second task is to remove the stop words present in text if any. The most common stop words in Hindi include ko, ki, ka etc. This ensures that the accuracy of classifier is not hampered due to unnecessary unimportant words. The third and final task is the conversion of all alphabets to lower case. This enables a faster and uniform output of the classifier. 4. The classifier is trained using SVM model. The training data set consists of sentences of similar type as the input but along with their actual correct action. The class labels represent the action to be performed by the application. The output of the classifier is a class label like call, message, app which denote calling, messaging, opening an application respectively. 5. This output is sent to the client where the dialog manager segregates the input user text into objects and combines it with the associated action it receives from the server. 6. The app uses implicit intents to perform the specified action on the object and completes the cycle. [4] FUTURE SCOPE 1. Support for all regional languages can be added to the proposed application in order to increase productivity and availability all across the globe. 2. Ability to perform all complex actions to make it more reliable and productive. 3. Ability to understand the intention and current environment of the user and perform action accordingly i.e. to make a context aware application to increase the efficiency[4]. 4. Smart suggestions to the user relative to the query can be added to make the system more intelligent[4]. 5. Instead of using ready-to-use API's, self developed speech recognition model using methods such as RNN and LSTM can be used for better accuracy. [5] CONCLUSION This paper discusses the need for developing an Android based personal assistant supporting regional languages. We proposed a system which uses machine learning techniques to do text categorization and perform basic functionalities of an Android user such as calling, messaging, opening applications, setting alarms and other simple miscellaneous tasks. The system will also generate appropriate responses relative to the input query thereby making it interactive and efficient. The system makes use of client server architecture in order to reduce the overhead on phones due to limited resource capability, thus making it more responsive and efficient. Nikita Goyal, Umang Bhate, Varun Punamiya, Aditi Gagrani, Venkatesan N 4

5 REFERENCES [1] Asha Bharambe, Adwait Vyas, Vedant Pandit, Chinmay Pai, Sanjay Wadhwa, "Hindi Language Personal Assistant For Android", International Journal of Computer Engineering and Applications, Volume IX, Issue III, April 15. [2] Charu C. Aggarwal, "A SURVEY OF TEXT CLASSIFICATION ALGORITHMS", IBM T. J. Watson Research Center Yorktown Heights, NY. [3] Anurag Sarkar, Saptarshi Chatterjee, Writayan Das, Debabrata Datta, "Text Classification using Support Vector Machine", International Journal of Engineering Science Invention ISSN, November [4] Ming Sun, Yun-Nung Chen, Alexander I. Rudnicky, "An Intelligent Assistant for High- Level Task Understanding", School of Computer Science, Carnegie Mellon University, USA. [5] Thorsten Joachims, "Text Categorization with Support Vector Machines: Learning with Many Relevant Features", University at Dortmund Informatik LS8, Germany, [6] Amitkumar O. Panchal, "Speech recognition using Recurrent Neural network", IJIRT, Volume 2 Issue 5, October [7] Mazin Gilbert, Iker Arizmendi, Enrico Bocchieri, Diamantino Caseiro, Vincent Goffin, Andrej Ljolje, Mike Phillips1, Chao Wang1, Jay Wilpon, "Your Mobile Virtual Assistant Just Got Smarter!", AT&T Labs-Research, Florham Park, NJ Vlingo, Cambridge, MA Nikita Goyal, Umang Bhate, Varun Punamiya, Aditi Gagrani, Venkatesan N. 5