Project On Gender Recognition Using Voice In R Language

In this Gender Recognition using voice in R, we'll build a machine learning model that can predict a speaker’s gender using extracted voice features.

The project will focus on audio feature analysis and classification techniques, using large datasets for accurate predictions. You’ll understand audio processing, SVM, Random Forest, and Neural Networks using libraries like caret, tuneR, and seewave. 

This project has its uses in voice assistants, call center automation, and audio-based classification systems, making it ideal for those exploring AI in audio.

Zoom ahead in your data science career with upGrad’s premier Online Data Science Courses. Learn Python, Machine Learning, AI, Tableau, and SQL from top industry experts—designed for real-world impact. Start your transformation today.

Find More Interesting R Projects: Top 25+ R Projects for Beginners to Boost Your Data Science Skills in 2025 

What Should You Know Before Starting the Gender Recognition Using Voice Project in R?

Before starting any project, having prior knowledge about certain concepts, tools, and what data type is used is important. Below are some things to know before starting the Gender Recognition Using Voice Project.

• Basic R Programming
  You need to have prior basic experience of working with data frames, variables, functions, and installing/loading libraries.
• Fundamentals of Machine Learning
  Having prior knowledge of classification tasks, model training, testing, and evaluation metrics like accuracy and confusion matrix.
• Working with CSV Files
  You should know how to load, inspect, and preprocess data from .csv files in R.
• Concept of Features and Labels
  You also need to understand what features (input variables) and labels (output classes like Male/Female) mean in supervised learning.
• Understanding of Audio Data (High-Level)
  Having a basic idea of voice-related features (like frequency, pitch, meanfun) even if raw audio isn’t being used.
• Train-Test Split Logic
  You must understand the importance of dividing data to assess model performance fairly.
• Libraries You'll Use
  You must be prepared to work with caret, e1071, randomForest, and optionally nnet, tuneR, and seewave.

Take your career in Data Science and AI to the next level with upGrad’s industry-aligned online programs. Gain in-demand expertise in Generative AI, Machine Learning, and Advanced Analytics—guided by top professionals. Enrol today and lead the innovation.

• Master’s Degree in Artificial Intelligence and Data Science From OPJGU
• Executive Diploma in Data Science & AI From IIITB
• MS in Data Science from Liverpool John Moores University

What Are The Tools, Technologies, and R Libraries Required For This Gender Recognition Using Voice Project

For every project, there are certain tools, libraries, and technologies used so that the project can provide optimal results. Below are the tools, libraries, and technologies used to successfully run this project.

Read More To Understand About: Machine Learning with R: Everything You Need to Know

Project Duration, Difficulty, and Skill Level Required For This Project

• Estimated Time Commitment
  2–4 hours for complete execution, model building, evaluation, and interpretation.
• Complexity Level
  It is a beginner-friendly project that is easy to follow, with optional intermediate tasks like hyperparameter tuning and audio feature engineering.

Step-by-Step Voice Gender Recognition Project Using R: A Detailed Guide

The section below will provide the step-by-step guide for this Gender Recognition by Voice Project.

Step 1: Install Required R Packages and load the dataset

Here, we will install all the necessary R libraries for machine learning, data manipulation, and audio processing (optional). These packages form the base of the project.

# Install the required packages
install.packages("caret")        # For ML model training and evaluation
install.packages("e1071")        # Required dependency for Support Vector Machine (SVM)
install.packages("randomForest") # For Random Forest classification and feature importance
install.packages("nnet")         # For building simple neural networks
install.packages("tidyverse")    # For data wrangling, visualization, and streamlined coding

# (Optional) For audio file processing – use only if working with raw .wav files
# install.packages("tuneR")      # For reading and analyzing audio signals
# install.packages("seewave")    # For audio signal feature extraction
# Read the uploaded CSV file
data <- read.csv("voice.csv")
# View the first few rows of the data
head(data)

The dataset will be shown in the output, consisting of rows and columns:

Here’s Something For You To Read: R For Data Science: Why Should You Choose R for Data Science?

Step 2: Explore the Dataset Structure and Distribution

This step helps you understand the dataset better by checking the column types, statistical summaries, and the distribution of gender labels. The code for this step is given below:

# Check the structure of the dataset (column types, etc.)
str(data)              # Shows column names, data types, and first few values for each variable
# See a quick summary of the dataset (min, max, mean, etc.)
summary(data)          # Provides descriptive statistics for each feature
# Check how many male and female samples there are
table(data$label)      # Displays the count of 'male' and 'female' entries in the target variable

After executing the code, it’ll return the result as:

female   male 

  1584   1584

This shows that there are 1,584 male and 1,584 female samples in this dataset.

Also Read: Data Preprocessing in Machine Learning: 11 Key Steps You Must Know!

Step 3: Prepare the Target Variable for Classification

In this step, we will convert the target variable (label) to a factor type, which is essential for classification models in R. The code for this section is given below:

# Convert the 'label' column to a factor (important for classification)
data$label <- as.factor(data$label)   # Change label to factor for ML algorithms to treat it as categorical
# Confirm the change
str(data$label)          # Check if 'label' is now a factor with two levels

This returns the output:

Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...

The output confirms that the label column has been successfully converted to a factor with two levels: "female" and "male". This is exactly what's needed for classification models like SVM, Random Forest, and Neural Networks in R.

Step 4: Split the Dataset into Training and Testing Sets

In this step, we divide the dataset into two parts. Here, 80% of the data will be used for training the model and 20% for testing it. This helps us evaluate the model on unseen data.

# Load caret library for splitting
library(caret)                           # caret provides tools for splitting and model training
# Set seed so results stay the same every time you run
set.seed(123)                            # Ensures reproducibility of your results
# Create a split (80% train, 20% test)
splitIndex <- createDataPartition(data$label, p = 0.8, list = FALSE)  # Stratified sampling
# Create training and test sets
trainData <- data[splitIndex, ]         # 80% of the data used for training
testData <- data[-splitIndex, ]         # Remaining 20% for testing the model

Output

Loading required package: ggplot2

Loading required package: lattice

We then move on to the next step after the caret package has loaded successfully.

Must Read: What’s Special About Machine Learning?

Step 5: Train and Evaluate the Support Vector Machine (SVM) Model

Here, we will train an SVM classifier using the training data and then test its performance on unseen test data using a confusion matrix. The code is given below:

# Load the SVM package
library(e1071)                                   # Contains the SVM function
# Train the SVM model using training data
svm_model <- svm(label ~ ., data = trainData)    # Fit the SVM classifier on training set
# Use the model to predict on the test data
svm_preds <- predict(svm_model, testData)        # Predict labels for test data
# Evaluate the model's performance
confusionMatrix(svm_preds, testData$label)       # View accuracy, precision, recall, etc.

After executing the code, we get the output as follows:

Confusion Matrix and Statistics

Reference

Prediction female male

female    309    4

male        7  312

Accuracy : 0.9826

95% CI : (0.9691, 0.9913)

No Information Rate : 0.5

P-Value [Acc > NIR] : <2e-16

Kappa : 0.9652

Mcnemar's Test P-Value : 0.5465

Sensitivity : 0.9778          

Specificity : 0.9873          

Pos Pred Value : 0.9872          

Neg Pred Value : 0.9781

Prevalence : 0.5000          

Detection Rate : 0.4889          

Detection Prevalence : 0.4953          

Balanced Accuracy : 0.9826

'Positive' Class : female

This output indicates:

Key Metrics from Confusion Matrix:

• Accuracy: 98.26%
• Sensitivity (Recall for Female): 97.78%
• Specificity (Recall for Male): 98.73%
• Kappa: 0.9652 (indicates strong agreement beyond chance)

 Interpretation:

• The model is very accurate and balanced in identifying both male and female voices.
• Only 11 misclassifications out of 632 test samples.

Step 6: Build and Evaluate a Random Forest Model

In this step, we will use the Random Forest algorithm, which is an ensemble of decision trees. It’ll be used to classify the speaker's gender. The code for this section is given below:

# Load the Random Forest package
library(randomForest)
# Train the Random Forest model
rf_model <- randomForest(label ~ ., data = trainData)
# Predict using the model
rf_preds <- predict(rf_model, testData)
# Evaluate model accuracy
confusionMatrix(rf_preds, testData$label)

Running this code will give us the output:

randomForest 4.7-1.2

Type rfNews() to see new features/changes/bug fixes.

Attaching package: ‘randomForest’

The following object is masked from ‘package:ggplot2’:

margin

Confusion Matrix and Statistics

Reference

Prediction female male

    female    310    8

    male        6  308

Accuracy : 0.9778          

95% CI : (0.9631, 0.9878)

No Information Rate : 0.5             

P-Value [Acc > NIR] : <2e-16          

Kappa : 0.9557

Mcnemar's Test P-Value : 0.7893

Sensitivity : 0.9810          

Specificity : 0.9747          

Pos Pred Value : 0.9748          

Neg Pred Value : 0.9809          

Prevalence : 0.5000          

Detection Rate : 0.4905          

Detection Prevalence : 0.5032          

Balanced Accuracy : 0.9778          

'Positive' Class : female

This output shows that the Random Forest model achieved 97.78% accuracy, which is excellent and very close to the SVM model's performance.

Read More: What Is Data Acquisition: Key Components & Role in Machine Learning

Step 7: Train and Evaluate a Neural Network Model

In this step, we will build a Neural Network model using the nnet package. We’ll train the model, predict the outcomes, and evaluate the performance using a confusion matrix. Here is the code for this section:

# Load neural network package
library(nnet)
# Train a basic neural network model
nn_model <- nnet(label ~ ., data = trainData, size = 5, maxit = 500, decay = 0.01)
# Predict using the model
nn_preds <- predict(nn_model, testData, type = "class")
# Convert predictions to factor with the same levels as the original data
nn_preds <- factor(nn_preds, levels = levels(testData$label))
# Evaluate model performance
confusionMatrix(nn_preds, testData$label)

The output of this step is:

# weights:  111

initial  value 2315.802858 

iter  10 value 1684.945149

iter  20 value 1555.708643

iter  30 value 1207.755223

iter  40 value 492.912793

iter  50 value 317.043397

iter  60 value 297.496780

iter  70 value 265.837801

iter  80 value 253.802245

iter  90 value 225.065694

iter 100 value 184.608466

iter 110 value 173.591431

iter 120 value 167.815037

iter 130 value 163.807516

iter 140 value 161.046387

iter 150 value 159.542546

iter 160 value 158.765066

iter 170 value 158.058391

iter 180 value 157.492944

iter 190 value 157.418919

iter 200 value 157.409389

iter 210 value 157.407508

iter 220 value 157.407318

final  value 157.407298 

converged

Confusion Matrix and Statistics

Reference

Prediction female male

    female    306    5

    male       10  311

Accuracy : 0.9763          

95% CI : (0.9612, 0.9867)

No Information Rate : 0.5             

P-Value [Acc > NIR] : <2e-16          

Kappa : 0.9525

Mcnemar's Test P-Value : 0.3017          

Sensitivity : 0.9684          

Specificity : 0.9842

Pos Pred Value : 0.9839

Neg Pred Value : 0.9688          

Prevalence : 0.5000          

Detection Rate : 0.4842

Detection Prevalence : 0.4921          

Balanced Accuracy : 0.9763          

'Positive' Class : female   

The output shows that the model achieved strong performance:

• Accuracy: 97.63
• Sensitivity (Recall for female): 96.84%
• Specificity (Recall for male): 98.42%

Also Read To Level Up Your Skills: What is Data Wrangling? Exploring Its Role in Data Analysis

Step 8: Feature Importance Visualization Using Random Forest

This final step helps you understand which audio features contributed the most to the model’s classification decision. Random Forests have built-in functionality to compute and visualize feature importance. Here is the code for this step:

# Make sure the randomForest package is loaded
library(randomForest)
# Plot the feature importance
varImpPlot(rf_model, main = "Feature Importance - Random Forest")

The output for this code is:

This chart shows which voice features are most important for predicting gender using the Random Forest model.

• Most important feature:
   meanfun (mean of the fundamental frequency), this is the main clue the model uses to tell if the voice is male or female.
• Other helpful features:
   IQR, Q25, and sd also help the model; these are related to how the voice pitch and variation behave.
• Least useful features:
  Things like maxfun, modindx, meandom, etc., didn’t help the model much.

The graph shows that the pitch and variation in the voice are the biggest factors in telling whether a voice is male or female. The model mostly uses those clues to make accurate predictions.

Conclusion

In this Voice Gender Recognition project, we used R in Google Colab to classify voices as male or female based on acoustic features like pitch, frequency, and energy. 

After preprocessing the data and splitting it into training and test sets, we trained three models: SVM, Random Forest, and Neural Network. Among them, the Random Forest model achieved the highest accuracy of 97.78%. 

The Feature importance analysis showed that variables like meanfun and IQR were the most influential. This project has shown the effectiveness of machine learning in accurately identifying gender from voice characteristics.

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

Top Data Science Skills to Learn

Stay informed and inspired with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!

Read our popular Data Science Articles

Reference:
https://colab.research.google.com/drive/17Pnp50p2zbQBnMgAEVLk5oDY6ueISNkS#scrollTo=oeKalkIYx7Jr