Voice Assistance using Android app and Raspberry Pi

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Description

Voice Assistance

Voice Assistance using Android app and Raspberry Pi – This project uses Raspberry Pi as the core, which is connected with the Internet. Voice command is given from the Android mobile by installing the android application in mobile. Mobile is connected with the Raspberry Pi via Bluetooth. These commands are received in Raspberry Pi and processed based on applications. It includes cloud-based applications like Weather reports and also it has more features like capturing the image while saying cheese, entertainment like talking tom, IoT commands like ping the Mail, taking short line notes which convert voice to text and save it in log file and Date and time requests also can be done.


INTRODUCTION

A virtual assistant is a well-known application, which includes all functionalities like cloud, IoT, and some assisting actions. This project helps you to begin with virtual assistance using android mobile which is interfaced with the Raspberry Pi. Android mobile is used only for mic interface so that you can give the voice commands through the Android mobile to the raspberry pi via Bluetooth.

Existing system

  • In the existing system, a voice-controlled robotic car or robotic arm is done using android mobile.
  • Google assistance is done with Raspberry pi by using a USB Microphone which is difficult to give voice commands from the distance.
  • You have to give voice commands by having close contact with Mic.

DISADVANTAGE

  • Limited for most of the applications since the distance from which command can be given is very low
  • Users need to speak directly into the mic

Proposed system

In this proposed system, more functionalities like image application, cloud function, IoT, and every feature emerge in this project. It’s planning to use a Bluetooth Android App to make it easier to give voice commands. The Raspberry Pi, being the center of the system, will be connected to an Internet-enabled Wi-Fi. Android mobile is used only for mic interface. Here it provides intelligence to devices powered by voice assistance. Using USB Speakers for the feedback system.

ADVANTAGES

  • Users can give voice commands from anywhere in the room
  • Easy to setup compared to old methods
  • It works in a more interactive way in the form of speech.

BLOCK DIAGRAM

 

Voice Assistance using Android app and Raspberry Pi 1

 

BLOCK DIAGRAM DESCRIPTION

  • Raspberry pi connects the whole system to connect the camera and speaker
  • Speaker is connected to the 3.5 mm audio jack of the Raspberry Pi
  • The monitor is connected with the HDMI port of Raspberry Pi
  • Android mobile is connected with the Raspberry Pi via Bluetooth
  • USB Camera is interfaced with the Raspberry Pi

CIRCUIT DIAGRAM

voice


PROJECT DESCRIPTION

Since this is the personal virtual assistance, Face recognition is also added, the assistant will work only if the Face gets authenticated, and after being recognized it will wait for the voice command. Based on certain functions received, applications can be performed. Face recognition works by using Haar cascade frontal face algorithm to recognize the Face. The system always looking for the face, if it matches it’s ready to assist the person. Voice command is given from the Android mobile based on command applications are performed automatically.

HARDWARE REQUIREMENTS

  • Raspberry Pi
  • Speaker
  • USB camera

SOFTWARE REQUIREMENTS

  • Program: Python
  • Platform: Python 3 IDLE
  • Raspberry pi os: Raspian os
  • Library: OpenCV
  • ?Pi3 Bluetooth manager? android app

REFERENCES

[1] D. A. Ferrucci. Introduction to ?this is watson?. IBM Journal of Research and Development, 56(3.4):1:1?1:15, May 2012.

[2] Joseph Weizenbaum. Eliza – a computer program for the study of natural language communication between man and machine. Commun. ACM, 9(1):36?45, January 1966.

[3] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. CoRR, abs/1409.3215, 2014.

[4] Yoshua Bengio, R?ejean Ducharme, Pascal Vincent, and Christian Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3:1137?1155, March 2003.

[5] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.


 

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