Automatic Emotion Recognition from Speech Using Machine Learning with Gender Dependent Databases using matlab signal processing


ABSTRACT:

In this paper, we study how speech features numbers and statistical values impact recognition accuracy of emotions present in speech. With Gaussian Mixture Model (GMM), we identify two effective features, namely Mel Frequency Cepstrum Coefficients (MFCCs) extracted directly from speech signal. Using GMM supervector formed by values of MFCCs, delta MFCCs and ACFC, we conduct experiments with Berlin emotional database considering six previously proposed emotions: anger, disgust, fear, happy, neutral and sad. Our method achieve emotion recognition rate of 74.45%, significantly better than 59.00% achieved previously. To prove the broad applicability of our method, we also conduct experiments considering a  gender and different set of emotions: anger, boredom, fear, happy, neutral and sad. Our emotion recognition rate of 75.00% is again better than71.00% of the method of hidden Markov model with MFCC, delta MFCC, cepstral coefficient and speech energy. 

EXISTING SYSTEM: 

The existing system used to classify only male and female gender based on speech recognition and similarly emotions detection is done using feature extraction.

DISADVANTAGES:

1. Only classifies the gender 
2. In terms of signal ,only removing noises
3. Less PSNR.

PROPOSED SYSTEM:

The proposed system combines both  gender classification and their emotions using MFCC and HMM .The MFCC and HMM enhances the accuracy as compared to the existing which is done separately.

ADVANTAGES:

1. Combining both gender classification and emotion recoginition
2. PSNR is more
3. Less complex


FLOW DIAGRAM:

ALGORITHM :

1. Mel-frequency cepstral coefficients (MFCCs)
2. Hidden Markov Model (HMM)
3. Multi Support vector machine (SVM)

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk                                : 20 GB HD

SOFTWARE REQUIREMENTS

Operating System           : Windows 7 
Software Programming Package           : MATLAB R2014a


REFERENCES:

1.D. Reynolds, "Gaussian Mixture Models", Encyclopedia of Biometric Recognition, Springer, Feb. 2008.