Voice Gender Recognition

Gender Recognition by Voice and Speech Analysis

This project trains a computer program to identify a voice as male or female, based upon acoustic properties of the voice and speech. The model is trained on a dataset consisting of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R and then processed with artificial intelligence/machine learning algorithms to learn gender-specific traits for classifying the voice as male or female.

The best model achieves an accuracy of 100% on the training set and 89% on the test set.

Update: By narrowing the frequency range analyzed to 0hz-280hz (human vocal range), the best accuracy is boosted to 100%/99%.

The dataset used is a CSV file containing the following fields:

"meanfreq","sd","median","Q25","Q75","IQR","skew","kurt","sp.ent","sfm","mode","centroid","meanfun","minfun","maxfun","meandom","mindom","maxdom","dfrange","modindx","label"

In addition to the pre-processed dataset, the raw voice samples used for training are included as .WAV files in a separate repository. The .WAV files are pre-processed in R to produce the above dataset.

Accuracy

The trained models have achieved the following accuracies (train/test):

Baseline Algorithm (always male)

50%/50%

Baseline Algorithm (simple frequency threshold)

61%/59%

Logistic Regression

72%/71%

Classification and Regression Tree (CART)

81%/78%

Random Forest

100%/87%

Generalized Boosted Tree Regression

91%/84%

XGBoost

100%/87%

XGBoost (Updated with frequency range 0hz-280hz)

100%/99%

Acoustic Properties Measured

The following acoustic properties of each voice are measured:

• duration: length of signal
• 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

MIT Licensed