Emotional speaker recognition based on i-vector through Atom Aligned Sparse Representation
Li Chen, Yingchun Yang · 2013
I-vector algorithm was previously adopted to improve the performance of ASR (Automatic Speaker Recognition) system which is degraded by emotion variability. The variability compensation technique is LDA (Linear Discriminant Analysis) which assumes the variability is speaker-independent. However, this assumption is not suitable for emotion variability because we discover that the pattern of emotion variability is speaker-dependent. Therefore, a novel emotion synthesis algorithm AASR (Atom Aligned Sparse Representation) is proposed to characterize this speaker-dependent pattern and compensate the emotion variability within i-vectors. The experiments conducted on MASC show that our algorithm, compared with the GMM-UBM algorithm and the conventional variability compensation algorithm LDA, both can enhance the speaker identification and verification performances.