This repository is about building a deep learning model using TensorFlow 2 to recognize gender of a given speaker's audio. Read this tutorial for more information.

Nov 1, 2017 · Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data.

Jul 16, 2018 · Here are 6 public repositories matching this topic... Using machine learning to recognise gender by analysing recorded voice. Add a description, image, and links to the gender-recognition-by-voice topic page so that developers can more easily learn about it.

Apr 13, 2023 · Machine learning (ML) is a branch of Artificial intelligence (AI) in which algorithms improve themselves over a large amount of data. The aim of this project is to experiment and evaluate...

This paper will inspect the efficiency and significance of machine learning algorithms to the voice-based gender identification problem. Need Help? Gender identification is considered to be one of the major problems in the field of signal processing. Formerly, …

Introduction Voice gender detection is the process of automatically identifying the gender of speakers based on their recorded speech. This project utilizes machine learning techniques to classify voice recordings into male or female categories.

Feb 21, 2024 · The experiments are implemented by two techniques to split speech dataset: train_test_split function and 10-fold Cross-Validation on the training set (after splitting the data into train and test...

The gender recognition system by a sample of voice has an excellent mechanism based on many factors and features of sound sig- nals like frequency, pitch, and loudness. This paper implements a machine to distinguish between the male's and female's voice.

Gender identification represents a fundamental component of speech recognition and automatic interacting sound responding systems. Identifying the voice gender minimizes the computational loads of these systems for additional processing.

In this study, we used various models to improve accuracy, one of which was by using deep learning with the voice gender DNN method. This noise reduction uses the extraction feature of the Mel Frequency Cepstral Coefficient (MFCC), then the sound classification uses SVM. By using a separation ratio of 80% for training data and 20% for testing data.