3,581 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
Substructure and Boundary Modeling for Continuous Action Recognition
This paper introduces a probabilistic graphical model for continuous action
recognition with two novel components: substructure transition model and
discriminative boundary model. The first component encodes the sparse and
global temporal transition prior between action primitives in state-space model
to handle the large spatial-temporal variations within an action class. The
second component enforces the action duration constraint in a discriminative
way to locate the transition boundaries between actions more accurately. The
two components are integrated into a unified graphical structure to enable
effective training and inference. Our comprehensive experimental results on
both public and in-house datasets show that, with the capability to incorporate
additional information that had not been explicitly or efficiently modeled by
previous methods, our proposed algorithm achieved significantly improved
performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure
An improved StarGAN for emotional voice conversion: enhancing voice quality and data augmentation
Emotional Voice Conversion (EVC) aims to convert the emotional style of a
source speech signal to a target style while preserving its content and speaker
identity information. Previous emotional conversion studies do not disentangle
emotional information from emotion-independent information that should be
preserved, thus transforming it all in a monolithic manner and generating audio
of low quality, with linguistic distortions. To address this distortion
problem, we propose a novel StarGAN framework along with a two-stage training
process that separates emotional features from those independent of emotion by
using an autoencoder with two encoders as the generator of the Generative
Adversarial Network (GAN). The proposed model achieves favourable results in
both the objective evaluation and the subjective evaluation in terms of
distortion, which reveals that the proposed model can effectively reduce
distortion. Furthermore, in data augmentation experiments for end-to-end speech
emotion recognition, the proposed StarGAN model achieves an increase of 2% in
Micro-F1 and 5% in Macro-F1 compared to the baseline StarGAN model, which
indicates that the proposed model is more valuable for data augmentation.Comment: Accepted by Interspeech 202
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