1,355 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Survey of deep representation learning for speech emotion recognition
Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
Generative Adversarial Networks (GANs) have gained a lot of attention from
machine learning community due to their ability to learn and mimic an input
data distribution. GANs consist of a discriminator and a generator working in
tandem playing a min-max game to learn a target underlying data distribution;
when fed with data-points sampled from a simpler distribution (like uniform or
Gaussian distribution). Once trained, they allow synthetic generation of
examples sampled from the target distribution. We investigate the application
of GANs to generate synthetic feature vectors used for speech emotion
recognition. Specifically, we investigate two set ups: (i) a vanilla GAN that
learns the distribution of a lower dimensional representation of the actual
higher dimensional feature vector and, (ii) a conditional GAN that learns the
distribution of the higher dimensional feature vectors conditioned on the
labels or the emotional class to which it belongs. As a potential practical
application of these synthetically generated samples, we measure any
improvement in a classifier's performance when the synthetic data is used along
with real data for training. We perform cross-validation analyses followed by a
cross-corpus study.Comment: 5 pages, Accepted to Interspeech, Hyderabad-201
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