A voice and speaker recognition system using machine learning

• Data
• Description
• Machine Learning Process
• Task-Info
• Preview

Data

• Every member of the team recorded equal number of voice records with equal number of categories.
• Hierarchy:
  • DSP_Data_New
    • blank (blank audios)
    • close the door
    • open the door
    • unlock the door
  • DSP_Data_Verification
    • open the door (password)
    • others

Description

• This is a web app that can recognize speech and verify voices in a form of Voice Command Door Lock that opens if the owners say the correct password "open the door".
• The web page is an E-poster contains some information about the used data features, ML pipeline, the decision tree of the model , a section to test the app and a pie chart to show the confidence score of the result.

Machine Learning Process

• We have followed the full machine learning pipeline used in the industry, from data acquisition to models deployment, and here are the steps:

  • 1- Data Acquisition

  • 2- Data Augmentation :to increase the amount of data to be trained by generating new data points from existing data to improve the performance and outcomes of the model.

  • 3- Exploratory Data Analysis "EDA" : to analyze the data using visual techniques that led us to take all features in frequency domain and only two features (AE,RMSE) in time domain

    note: we mainly have two models ,one for speech recognition and the other for voice verifying, the next steps are applied to both of them.

  • 4- Feature extraction and Dimensionality reduction : to reduce the number of features and only take the most efficient ones.

  • 5- Building The model: according to the accuracy we used random forest with its best hyper-parameters, the accuracy of the voice verifying model was 84% and the accuracy of the speech recognition model was 60% and because it's relatively low we imported an external model that convert speech to text to detect the password.

  • 6- Models deployment

Task-Info

• Digital Signal Processing (SBE3110) class task 4 created by Team 9:

  Names	Section	Bench Number
  Mahmoud Yaser	2	30
  Ahmed El Sarta	1	8
  Adham Mohamed	1	9
  Maha Medhat	2	38

• Languages & Frameworks

  • Python (Machine Learning)
  • HTML, CSS, JavaScript (Frontend)
  • Flask (Backend)

• Submitted to: Dr. Tamer Basha & Eng. Abdallah

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