Applying emotional factor analysis and I-vector to emotional speaker recognition

Authors:
Li Chen;Yingchun Yang
Affiliations:
College of Computer Science & Technology, Zhejiang University, Hangzhou, China;College of Computer Science & Technology, Zhejiang University, Hangzhou, China
Venue:
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Year:
2011

Citing 3
Cited 2

Front-End Factor Analysis for Speaker Verification

IEEE Transactions on Audio, Speech, and Language Processing
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition

IEEE Transactions on Audio, Speech, and Language Processing
Speaker and Session Variability in GMM-Based Speaker Verification

IEEE Transactions on Audio, Speech, and Language Processing

Emotional speaker identification by humans and machines

CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Toward emotional speaker recognition: framework and preliminary results

CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition

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Abstract

Emotion variability is an important factor that degrades the performce of speaker recognition system. This paper borrows ideas from Joint Factor Analysis (JFA) algorithm based on the similarity between emotion effect and channel effect and develops Emotional Factor Analysis (EFA) into solving the emotion variability problem. I-Vector is appiled also. The experiment carried on MASC (Madarin Affective Speech Corpus) shows that EFA and I-Vector method can bring an IR increase of 7%-10% and an EER reduction of 3%-4% compared with the GMM-UBM system.