1. PROJECT PROPOSAL
Shamalee Deshpande

2. Problem Statement
Extracting soft biometric features
Age
Gender
Accent

3. Speaker Database A Speaker database from the LDC Corpus Catalog*
Preferable use half the speaker set for training and the later half for verification of results
Contain varying gender, age and accent
*Linguistic Data Consortium,

4. Possible Computation for Gender
Pitch
In Cepstrum Analysis, Formants are completely removed from the spectrum thus isolating the pitch frequency. LPC also used to find pitch
Pitch is used to classify speech with regards to Gender
Av Males= Hz Av Females= Hz
Window
DFT
IDFT
Speech
Cepstrum
LOG

5. Possible Computation for Accent
People usually have characteristic styles of pronouncing phonemes from an early age dependant on the primary language learned.
Cepstral coefficients may again be used and presumably the MFCCs for the analysis of the speech spectrum to identify local/non-local speakers in a database.

6. Possible Computation for Age
BUZZER
Glottal excitation
TUBE
Vocal tract
Characterized by intensity and pitch
Characterized by formants
Vocal tract length is said to be a good classifier of the age of a speaker
Formant frequencies derived using LPC co-relate to the length of the vocal tract
Children are said to have a higher formant frequency range than adults
Specifically, elderly speakers are said to have lower formant frequencies F1,F2,F3 than their younger counterparts more so seen with regards to F1