Gender-Recognition-by-Voice

Gender Recognition by Voice and Speech Analysis by Implementing Classification Algorithms in Python

Objective

To predict gender with corresponding voice and speech features.

Dataset

This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

Variable Description

The following acoustic properties of each voice are measured and included within the CSV:

• label(Target Variable): male or female
• meanfreq: mean frequency (in kHz)
• sd: standard deviation of frequency
• median: median frequency (in kHz)
• Q25: first quantile (in kHz)
• Q75: third quantile (in kHz)
• IQR: interquantile range (in kHz)
• skew: skewness (see note in specprop description)
• kurt: kurtosis (see note in specprop description)
• sp.ent: spectral entropy
• sfm: spectral flatness
• mode: mode frequency
• centroid: frequency centroid (see specprop)
• peakf: peak frequency (frequency with highest energy)
• meanfun: average of fundamental frequency measured across acoustic signal
• minfun: minimum fundamental frequency measured across acoustic signal
• maxfun: maximum fundamental frequency measured across acoustic signal
• meandom: average of dominant frequency measured across acoustic signal
• mindom: minimum of dominant frequency measured across acoustic signal
• maxdom: maximum of dominant frequency measured across acoustic signal
• dfrange: range of dominant frequency measured across acoustic signal
• modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range

Results

Decision Trees

Accuracy on training set: 1.000
Accuracy on test set: 0.961

Random Forests

Accuracy on training set: 0.998
Accuracy on test set: 0.976

Gradient Boosting

Accuracy on training set: 0.996
Accuracy on test set: 0.975

Support Vector Machines

Accuracy on training set: 0.985
Accuracy on test set: 0.984

Multilayer Perceptron

Accuracy on training set: 0.995
Accuracy on test set: 0.981