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

Computers, Materials & Continua 2023年第10期77卷 1009-1030页

作者： Somin Park Mpabulungi Mark Bogyung Park Hyunki Hong College of Software Chung-Ang UniversitySeoul06973Korea Department of AI Chung-Ang UniversitySeoul06973Korea

Speech emotion recognition is essential for frictionless human-machine interaction,where machines respond to human instructions with context-aware *** properties of individuals’voices vary with culture,language,gender,and *** 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 *** the proposed approach,two wav2vec-based modules(a speaker-identification network and an emotion classification network)are trained with the Arcface *** speaker-identification network has a single attention block to encode an input audio waveform into a speaker-specific *** 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 *** two representations are then fused into a single vector representation containing emotion and speaker-specific *** results showed that the use of speaker-specific characteristics improves SER ***,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)*** 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

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