Raspberry pi based Home Automation using Voice recognition

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Raspberry pi based Home Automation using Voice recognition

SKU: Raspberry pi based Home Automation using Voice recognition Category:

Description

ABSTRACT

Home Automation is conveniences installed and designed to perform chore in your living place. Smart homes are often referred to as intelligent homes as they perform services that become part of our life. Many of the automated systems that silently perform their jobs unnoticed this is automation at its best. The system is initially in standby mode waiting for an input from the user. Once an input is detected, it is analysed by the speech recognition module. If a known command is detected, the speech recognition system sends respective digital representations to the microcontroller. The microcontroller then interprets these data signals, compares them with a database and thus identifies the referred load and its desired state.

EXISTING SYSTEM

  • In the existing process, there is a remote based system will be used to control the home devices.
  • Next we will use the gesture recognition to activate the home appliances.
  • Normal LCD Display was used to display the current status of the devices

PROPOSED SYSTEM

  • In the proposed system, Voice recognition method was used to control the home appliances and all the devices.
  • Combined with the google assistance module for the purpose of 100 percentage accurate output.
  • The current status of the device isdisplayed in the GUI (Graphical User Interface)
  • Android application will be developed for the voice recognition based devices controlling.

BLOCK DIAGRAM

Raspberry pi based Home Automation using Voice recognition

HARDWARE REQUIREMENTS

  • Raspberry Pi 3
  • USB Mic
  • Relay-2
  • Light
  • Fan

SOFTWARE REQUIREMENTS

  • Operating system : Raspbian jessie
  • Programming language : Python 2.7
  • Python TKinter
  • Java

REFERENCES

1. S. Ruan et al., “Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones,” Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4, 2016; doi.org/10.1145/3161187.

2. D. Amodei et al., “Deep Speech 2: End-to-End Speech Recognition in English and Mandarin,” Proc. 33rd Int’l Conf. Machine Learning (ICML 16), 2016, pp. 173–182.

3. B. Suhm, “Towards Best Practices for Speech User Interface Design,” Proc. 8th European Conf. Speech Communication and Technology (Interspeech 03), 2003, pp. 2217–2220.

4. A.I. Rudnicky, “Speech Interface Guidelines,” 1996; http://www.speech.cs.cmu.edu/air/papers/SpInGuidelines/SpInGuidelines.html.

5. N. Yankelovich, G.-A. Levow, and M. Marx, “Designing SpeechActs: Issues in Speech User Interfaces,” Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI 95), 1995, pp. 369–376.

Additional information

Weight 1.000000 kg

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