1. Command Me Specification
Shawn Mathew, Anthony Tan, Amy Wong

2. Background Information - Speech Recognition
Automatic speech recognition systems takes a speech signal and converts into words for natural processing language
Process:
Digital Sampling
Acoustic Signal Processing
Recognition of Words (Hidden Markov Modeling)
hidden Markov model is a tool for representing probability distributions over sequences of observations

3. Background Information - Natural Language Processing
What is natural language?
Natural language is stated to be any language that are formed by humans and has evolved naturally through human use.
It can be categorized as speech, writing, or signing (sign language).
What is natural language processing?
The study of this language and its interaction with computers.

4. Researched uses for Natural Language Processing Algorithms
These are some of the tasks that have been researched for Natural Language Processing
Summarizing chunks of text
Translating one human language into another
Converting information that is understandable to computers into readable language for humans
Determining the part of speech of a word
Converting images of text into machine understandable text

5. Project Idea
Our project idea revolves around taking a verified human speech and converting that into an action that will be performed by a computer.
Step 1: Provide computer with a sound clip from a person speaking.
Step 2: Convert that sound representation into text.
Step 3: The computer will perform a certain task/action based on the textual representation that was derived from human speech
For now, we are focusing on commands for a computer such opening a certain program or searching what the weather is like through the web
Over time, we may improve through machine learning such that the computer will learn new commands that are given to it instead of choosing from a set of predetermined tasks.

6. Project Implementations
Take a saying and the speaker’s voice from microphone
Turn the saying into text
Text needs to be tokenized
Tokenized text should be passed into machine learning algorithm or set of if statements
Algorithm will decide what should be executed

7. Parsing Text

8. Algorithm After Tokenizer
Machine Learning?
Train the machine
If Statements?
Key words connected with commands

9. Additional Implementation
Add voice recognition for user
Commands may change according to user

10. Libraries Bob.spear (Speaker recognition Python toolkit)
CMU Sphinx (Voice to Text)
Natural Language Toolkit (Tokenizer)
Keras (Machine Learning)
Chatterbox (take text and provide response)

11. Sources
files/full_papers/020513_Reynolds.pdf
n.pdf