This document presents a system for gender recognition from speech signals using feature extraction and pattern matching. The objective is to recognize gender using different speech signals. Power spectrum is extracted from the FFT applied signal and used as a feature. MATLAB is used for feature extraction, where speech signals are transformed to the power spectrum via FFT. Frequency is then used as a classifier to determine if the input speech is from a male or female based on thresholds in the power spectrum. The proposed system achieves 80% accuracy in recognizing gender from 10 speech samples.

1. Advanced Gender Recognition System
Using Speech Signal
Done by, Guided by,
Prabha M Ms. G.Bharatha Sreeja M.E.
(963212106044) Assistant Professor
Viveka P Department of ECE.
(963212106073)
Department of ECE.

2. OBJECTIVE:
• To recognize the gender using different speech signal.
• Power Spectrum used as feature and it is extracted from the
FFT applied signal.
• It is used to increase the accuracy of gender recognition.

3. INTRODUCTION:
 Speech signal is used to communicate among people not only caries the
information but also consists information of the particular speaker.
 The information are social factors, affective factor and the properties of
the physical voices production apparatus for which human beings are able
to recognize whether the speaker is male or female.
 The meaning of Gender Recognition (GR) is recognizing the gender of
the person whether the person is male or female.

4. APPLICATIONS:
 Financial Transaction.
 Gender Sensitive Surveys.
 To enhance Speaker Adaptation.
 Modern Voice Password Technology.

5. SOFTWARE USED:
• MATLAB – MAtrix LABoratory.
• It is a powerful language for technical computing.
• Its basic data element matrix(array)
• Used for math computations, modeling and simulations, data
analysis and processing, visualization and graphics.

6. PROPOSED SYSTEM:
Input
Speech Feature Extraction
Feature Extraction
Input
Speech
Store Extracted
Feature (data) in
Database
Pattern
Matching
Decision Decision Logic

7. FEATURE EXTRACTION:
Speech
Signal
Drive Data from Recorded
Wave Sound
FFT
Power spectrum
Log10|.|2
Sample point at maximum power spectrum
Frequency determination at max power

8. RESULTS
Input Signals:
Female:
Male:

9. POWER SPECTRUM

10. TABULATION:
Parameter Fundamental frequency
Threshold Value 3.8335 e-006
Male 2.652 e-006
Female 5.924 e-006

11. RECOGNITION RESULTS:
No. of speaker No. of accuracy of
gender
Recognize
percentage (%)
1 Male 100
2 Male 0
3 Male 100
4 Male 100
5 Male 100
6 Female 100
7 Female 100
8 Female 100
9 Female 100
10 Female 0

12. CONCLUSION:
• FFT and Power Spectrum are used as Feature.
• Frequency is used as Classifier.
• Recognition Accuracy is 80%.
• In future, more features can be added like MFCC, Formants.

13. REFERENCES:
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2011.
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Engineering and Technology., vol. 1, Issue 2, pp. 51-63, 2011.

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