Cross-Corpus Speech Emotion Recognition Based on Hybrid Neural Networks

Rehman, Abdul; Liu, Zhentao; Li, Danyun; Wu, Bao-Han

doi:10.23919/ccc50068.2020.9189368

Cited by 1 publication

(1 citation statement)

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“…For cross-corpus experiments, all corpora were combined together, thereby producing 11 h 45 min for training, 1 h 30 min each for validation and testing. In Rehman et al ( 2020 ), to develop a more adaptable SER in adversarial conditions, they presented a hybrid neural network framework for cross-corpus SER. The hybrid neural network consisted of two-layer LSTMs and a ramification layer.…”

Section: Deep Learning Methods For Cross-corpus Sermentioning

confidence: 99%

Deep Cross-Corpus Speech Emotion Recognition: Recent Advances and Perspectives

et al. 2021

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Automatic speech emotion recognition (SER) is a challenging component of human-computer interaction (HCI). Existing literatures mainly focus on evaluating the SER performance by means of training and testing on a single corpus with a single language setting. However, in many practical applications, there are great differences between the training corpus and testing corpus. Due to the diversity of different speech emotional corpus or languages, most previous SER methods do not perform well when applied in real-world cross-corpus or cross-language scenarios. Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have increasingly been adopted for cross-corpus SER. This paper aims to provide an up-to-date and comprehensive survey of cross-corpus SER, especially for various deep learning techniques associated with supervised, unsupervised and semi-supervised learning in this area. In addition, this paper also highlights different challenges and opportunities on cross-corpus SER tasks, and points out its future trends.

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Section: Deep Learning Methods For Cross-corpus Sermentioning

confidence: 99%