Using Speaker-Specific Emotion Representations in Wav2vec 2.0-Based Modules for Speech Emotion Recognition  

作　　者：Somin Park Mpabulungi Mark Bogyung Park Hyunki Hong 

机构地区：[1]College of Software,Chung-Ang University,Seoul,06973,Korea [2]Department of AI,Chung-Ang University,Seoul,06973,Korea

出　　处：《Computers, Materials & Continua》2023年第10期1009-1030,共22页计算机、材料和连续体（英文）

基　　金：supported by the Chung-Ang University Graduate Research Scholarship in 2021.

摘　　要：Speech emotion recognition is essential for frictionless human-machine interaction,where machines respond to human instructions with context-aware actions.The properties of individuals’voices vary with culture,language,gender,and personality.These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition(SER).This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models.In the proposed approach,two wav2vec-based modules(a speaker-identification network and an emotion classification network)are trained with the Arcface loss.The speaker-identification network has a single attention block to encode an input audio waveform into a speaker-specific representation.The emotion classification network uses a wav2vec 2.0-backbone as well as four attention blocks to encode the same input audio waveform into an emotion representation.These two representations are then fused into a single vector representation containing emotion and speaker-specific information.Experimental results showed that the use of speaker-specific characteristics improves SER performance.Additionally,combining these with an angular marginal loss such as the Arcface loss improves intra-class compactness while increasing inter-class separability,as demonstrated by the plots of t-distributed stochastic neighbor embeddings(t-SNE).The proposed approach outperforms previous methods using similar training strategies,with a weighted accuracy(WA)of 72.14%and unweighted accuracy(UA)of 72.97%on the Interactive Emotional Dynamic Motion Capture(IEMOCAP)dataset.This demonstrates its effectiveness and potential to enhance human-machine interaction through more accurate emotion recognition in speech.

关 键 词：Attention block IEMOCAP dataset speaker-specific representation speech emotion recognition wav2vec 2.0 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]