Graduation Year


Document Type




Degree Name

MS in Electrical Engineering (M.S.E.E.)


Electrical Engineering

Degree Granting Department

Electrical Engineering

Major Professor

Ravi Sankar, Ph.D.

Co-Major Professor

Wilfrido Moreno, Ph.D.

Committee Member

Wilfrido Moreno, Ph.D.

Committee Member

Ismail Uysal, Ph.D.


MFCC, Pitch, Pre-Processing, SDC, SVMs


Automatic age and gender recognition for speech applications is very important for a number of reasons. One of the reasons is that it can improve human-machine interaction. For example, the advertisements can be specialized based on the age and the gender of the person on the phone. It also can help identify suspects in criminal cases or at least it can minimize the number of suspects. Some other uses of this system can be applied for adaptation of waiting queue music where a different type of music can be played according to the person's age and gender. And also using this age and gender recognition system, the statistics about age and gender information for a specific population can be learned. Machine learning is part of artificial intelligence which aims to learn from data. Machine Learning has a long history. But due to some limitations, for ex. , the cost of computation and due to some inefficient algorithms, it was not applied to speech recognition tasks. Only for a decade, researchers started to apply these algorithms to some real world tasks, for ex., speech recognition, computer vision, finance, banking, robotics etc. In this thesis, recognition of age and gender was done using a popular machine learning algorithm and the performance of the system was compared. Also the dataset included real -life examples, so that the system is adaptable to real world applications. To remove the noise and to get the features of speech examples, some digital signal processing techniques were used. Useful speech features that were used in this work were: pitch frequency and cepstral representations.

The performance of the age and gender recognition system depends on the speech features used. As the first speech feature, the fundamental frequency was selected. Fundamental frequency is the main differentiating factor between male and female speakers. Also, fundamental frequency for each age group is different. So in order to build age and gender recognition system, fundamental frequency was used. To get the fundamental frequency of speakers, harmonic to sub harmonic ratio method was used. The speech was divided into frames and fundamental frequency for each frame was calculated. In order to get the fundamental frequency of the speaker, the mean value of all the speech frames were taken. It turns out that, fundamental frequency is not only a good discriminator gender, but also it is a good discriminator of age groups simply because there is a distinction between age groups and the fundamental frequencies. Mel Frequency Cepstral Coefficients (MFCC) is a good feature for speech recognition and so it was selected. Using MFCC, the age and gender recognition accuracies were satisfactory. As an alternative to MFCC, Shifted Delta Cepstral (SDC) was used as a speech feature. SDC is extracted using MFCC and the advantage of SDC is that, it is more robust under noisy data. It captures the essential information in noisy speech better. From the experiments, it was seen that SDC did not give better recognition rates because the dataset did not contain too much noise. Lastly, a combination of pitch and MFCC was used to get even better recognition rates. The final fused system has an overall recognition value of 64.20% on ELSDSR [32] speech corpus.