1. Study of Word-Level Accent Classification and Gender Factors
CSCE 666
Term Project Presentation
Study of Word-Level Accent Classification and Gender Factors
Xing Wang, Peihong Guo, Tian Lan, Guoyu Fu
Dec 11th, 2013

2. Background
Motivation: Accent Recognition(AR) helps improve Speech Recognition system and Speaker Identification system
Our work
Word-level Classifiers are built using different types of features, words and learning methods
Speaker variation affect the AR system, gender factor is considered in this work.

3. Outline Feature Processing Classifier Experiments Word Alignment
MFCC, Formants
Classifier
GMM, HMM
Experiments
Comparison of features, classifiers and words
Gender effect

4. Data preparation Feature Processing
Audios and corresponding phonetic transcriptions
Biographical data for speakers
Web crawler (Python)
All metadata stored in Database
Faster to locate and extract audio information

5. Alignment Feature Processing
The Penn Phonetics Lab Forced Aligner is used to segment words apart.
We are doing the word-based accent recognition system.

6. Feature Extraction Feature Processing Frame MFCCs Formants
Window size, 25ms
Window shift, 10ms
MFCCs
Formants

7. Classifiers
GMM
Number of components are determined via cross validation
EM Algorithm to train
HMM
Observation: MFCC or Formants
Hidden states: Single Gaussian component
EM algorithm for training

8. Comparison of Features
Experiments
Comparison of Features
Experiments Process
5-fold cross validation
Training and test
Repeat 5 times on random samples
HMM is fixed as the classifier
Features for comparison
MFCCs (c0~c12)
F0F1F2
F1F2

10. Comparison of Classifiers
Experiments
Comparison of Classifiers
MFCC is fixed as the approach to extract features
Classifiers for comparison
Non-temporal: GMM
Temporal: HMM

12. Comparison of Words Experiments
Factors of classification accuracies of different words
Certain vowels and consonants
C1-C2 trajectory of word ‘OF’

14. Experiments
Gender Effect
Gender classification

15. Thanks!
Q&A