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Applying Emotional Factor Analysis and I-Vector to Emotional Speaker Recognition

机译:情感因素分析和I-Vector在说话人识别中的应用

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摘要

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.
机译:情绪变异性是降低说话人识别系统性能的重要因素。本文基于情感效应和渠道效应之间的相似性,借鉴了联合因子分析(JFA)算法的思想,并发展了情感因子分析(EFA)技术来解决情感变异性问题。 Ⅰ-Ⅴ区也适用。对普通话语情感语料库进行的实验表明,与GMM-UBM系统相比,EFA和I-Vector方法可使IR增加7%〜10%,EER减少3%〜4%。

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