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Journal Article
Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets
- Authors
- Kyoung Ju Noh, Chi Yoon Jeong, Jiyoun Lim, Seungeun Chung, Gague Kim, Jeong Mook Lim, Hyuntae Jeong
- Issue Date
- 2021-03
- Citation
- Sensors, v.21, no.5, pp.1-18
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IE-MOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.
- KSP Keywords
- Audio classification, Bidirectional Long Short-Term Memory, Classification models, Long-short term memory(LSTM), Loss-based, Memory-based, Model generalization, Motion Capture Database, Multi-Domain, Multi-path, Proposed model
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
