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https://github.com/primaryobjects/voice-gender

Gender recognition by voice and speech analysis
https://github.com/primaryobjects/voice-gender

acoustic-properties ai artificial-intelligence data-science gender gender-recognition logistic-regression machine-learning neural-network signal speech vocal voice

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Gender recognition by voice and speech analysis

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README 

        
Voice Gender

------------



Gender Recognition by Voice and Speech Analysis



Run the online [demo](https://voicegender.herokuapp.com).



Read the full article [Identifying the Gender of a Voice using Machine Learning](http://www.primaryobjects.com/2016/06/22/identifying-the-gender-of-a-voice-using-machine-learning/).



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](https://en.wikipedia.org/wiki/Voice_frequency#Fundamental_frequency)), the best accuracy is boosted to 100%/99%.**



## The Dataset



Download the pre-processed [dataset](https://raw.githubusercontent.com/primaryobjects/voice-gender/master/voice.csv) as a CSV file.



The CSV file contains 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"



"label" corresponds to the gender classification of the sample. The remaining fields are acoustic properties, detailed [below](#acoustic-properties-measured).



In R, you can load the dataset file [data.bin](https://raw.githubusercontent.com/primaryobjects/voice-gender/master/data.bin) directly as a data.frame with the command ```load('data.bin')```.



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



## Classification and Regression Decision Tree



The following decision tree, produced by the CART model, provides a high-level overview of important properties of the voice samples that may determine a specific gender classification of male versus female.



![Screenshot 1](https://raw.githubusercontent.com/primaryobjects/voice-gender/master/images/voice-plot-1.png)



After narrowing the frequency range to 0hz-280hz with a sound threshold of 15%, the accuracy is boosted to near perfect, and the following CART model is described. Mean fundamental frequency serves as a powerful indicator of voice gender, with a threshold of 140hz separating male from female classifications.



![Screenshot 2](https://raw.githubusercontent.com/primaryobjects/voice-gender/master/images/voice-plot-2.png)



## References



[The Harvard-Haskins Database of Regularly-Timed Speech](http://www.nsi.edu/~ani/download.html)



[Telecommunications & Signal Processing Laboratory (TSP) Speech Database at McGill University](http://www-mmsp.ece.mcgill.ca/Documents../Downloads/TSPspeech/TSPspeech.pdf), [Home](http://www-mmsp.ece.mcgill.ca/Documents../Data/index.html)



[VoxForge Speech Corpus](http://www.repository.voxforge1.org/downloads/SpeechCorpus/Trunk/Audio/Main/8kHz_16bit/), [Home](http://www.voxforge.org)



[Festvox CMU_ARCTIC Speech Database at Carnegie Mellon University](http://festvox.org/cmu_arctic/)



## Copyright



Copyright (c) 2022 Kory Becker http://primaryobjects.com/kory-becker



## Author



Kory Becker

http://www.primaryobjects.com