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

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.

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

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

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

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

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

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

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

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

Experiments Gender Effect Gender classification

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