Gender Recognition by Voice

Voice-Based Gender User Tracking for Telecom Company XYZ is an innovative solution designed to automate the process of tracking and categorizing the gender of the users based on their voice samples. As the user count continues to grow, manually categorizing users has become a tedious and time-consuming task. To streamline this process, we've developed a machine learning model that leverages Random Forest with Principal Component Analysis (PCA) achieved the highest accuracy in gender recognition.

Table of Contents

• Introduction
• Problem Statement
• Installing & Importing Libraries
• Data Acquisition & Description
• Data Pre-Processing
• Exploratory Data Analysis
• Data Preparation
• Model Development & Evaluation
• Acknowledgments
• Contact Information

Introduction

• Company XYZ is a leading telecom company with 5 million users.
• They want to keep track of the number of male and female users, but as the user count increases, the task becomes more tedious.
• They want to automate the process of keeping track of male and female users using their voice.
• Their research and development teams are trying to understand the acoustic properties of the voice and speech so that they can use it to enhance the customer experience in their new product.

Problem Statement

Current Challenges:

• The current process is a manual classification of gender using their voice.
• This is very tedious and time-consuming as it needs to be repeated every time a new customer joins.
• We will try to automate the process of predicting the male or female voice using acoustic properties of the voice or speech rather than doing this manual work.

Project Deliverables

• Deliverable: Gender prediction using voice.
• Machine Learning Task: Classification
• Target Variable: label(Male/Female)

Installing & Importing Libraries

We have used several libraries in our project, including:

• pandas
• numpy
• matplotlib
• seaborn
• sklearn
• pydotplus

Data Acquisition & Description

Dataset Details

The dataset is divided into two parts: Train and Test sets.

Train Set:

• The train dataset contains 2851 rows and 22 columns.
• The last column label is the target variable.
• The training dataset can be downloaded from here.

Test Set:

• The test dataset contains 317 rows and 21 columns.
• The test set doesn’t contain the label column.
• It needs to be predicted for the test set.
• The test dataset can be downloaded from here.

Dataset Feature Description

The following acoustic properties of each voice are measured and included in the dataset:

Column Name	Description
Id	Unique Id
meanfreq	Mean frequency (in kHz) for the voice sample
sd	Standard deviation of the frequency
median	Median frequency (in kHz) for the voice sample
Q25	First quantile (in kHz)
Q75	Third quantile (in kHz)
IQR	Interquartile range (in kHz)
skew	Skewness of the voice sample
kurt	Kurtosis of the voice sample
sp.ent	Spectral entropy
sfm	Spectral flatness of the voice sample
mode	Mode frequency
centroid	Frequency centroid
peakf	Peak frequency (the frequency with the highest energy)
meanfun	Average of fundamental frequency measured across the acoustic signal
minfun	Minimum fundamental frequency measured across the acoustic signal
maxfun	Maximum fundamental frequency measured across the acoustic signal
meandom	Average of dominant frequency measured across the acoustic signal
mindom	Minimum of dominant frequency measured across the acoustic signal
maxdom	Maximum of dominant frequency measured across the acoustic signal
dfrange	Range of dominant frequency measured across the acoustic signal
modindx	Modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range
label	The label for the voice sample (male or female)

Data Pre-Processing

• We don't have any Null values in our Dataset.
• The average mean frequency for both males and females is 0.18 with a standard deviation of 0.02.
• The minimum mean frequency is 0.039363, and the maximum is 0.251124.
• The skew column looks highly skewed with a minimum skewness of 0.14 and a maximum of 34.
• The kurt column also looks highly skewed with a mean of 35, median of 8.25, and a maximum of 1309.61.
• maxdom and dfrange are also highly skewed. We will explore their distributions in the EDA section.

Exploratory Data Analysis

• skew and kurt are highly correlated, with a correlation coefficient of 0.98 (almost 1).
• meanfreq and median are positively correlated.
• meanfreq is also highly correlated with feature Q25.
• Q25 and IQR are highly negatively correlated.
• Q25 and sd are highly negatively correlated.

Data Preparation

We have used StandardScaler to scale our data to bring all the features to the same scale.

Model Development & Evaluation

We have deployed the following models:

Model Name	Train Acc.	Test Acc.
Logistic Regression	0.972	0.980
Decision Tree	1.0	0.977
Random Forest	1.0	0.984
Random Forest with PCA	1.0	0.992

Acknowledgments

Special thanks to @Accredian for helping me in the Code.

Contact Information

Open for questions and suggestions.

• You can contact me on Email - anmol.1591@outlook.com
• LinkedIn - www.linkedin.com/in/anmol-arora-71371318

Thank You