Skip to content
This repository has been archived by the owner on Oct 14, 2019. It is now read-only.
/ LTUAssistant Public archive

An AI assistant like Google Now, Siri, Cortana, etc. for an LTU senior project.

Notifications You must be signed in to change notification settings

Xyaneon/LTUAssistant

Repository files navigation

LTUAssistant

An AI assistant like Google Now, Siri, Cortana, etc. for an LTU senior project.

This project depends on the following:

  • espeak for audio output
  • stanford_corenlp_pywrapper for natural language processing
  • The Stanford CoreNLP jar files
  • Java (to run Stanford CoreNLP)
  • The Python SpeechRecognition module for voice input
    • This might also require that the PyAudio packages are installed. On Ubuntu, this can be done by running sudo apt-get install python-pyaudio python3-pyaudio.
    • It also may require flac command line tools. On Ubuntu, this can be done by running sudo apt-get install flac All of these are downloaded and installed by running setup.sh, as long as you have git installed.

It currently runs on Ubuntu-based Linux systems, though we would like to implement cross-platform functionality soon.

The main program is located in LTUAssistant.py. A string can be passed to it on the command line as an argument if you want to execute a specific command without speaking it.

Features

This AI assistant aims to be like other well-known AI assistants, but with a focus on being useful to students. Thus, the feature set will concentrate on this particular target user.

Some abilities the AI assistant will include are:

  • Ability to go to school websites via voice command
  • Ability to email professors and classmates using LTU webmail
  • Room locator for a given room number (i.e., building and floor)
  • Study assistant using user-defined "note cards" to help students prepare for exams and quizzes
  • Reminding the user of upcoming class times

This list may expand based on whatever we have time to implement.

How it works

This assistant is structured into three main parts:

  1. Speech-to-text module
  2. Natural language processing module
  3. Command execution and database module

Each part passes its results to the next, providing a logical program flow and making collaboration between our group members easier.