论文标题
领域适应深度强化学习对现实世界的语音情感识别
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
论文作者
论文摘要
由于语音情绪识别（SER），计算机可以以情感智能的方式理解并与人们互动。但是，可以显着改善SER在跨库中和现实世界中的实时数据馈送方案的性能。无法使现有模型适应新域是SER方法的缺点之一。为了应对这一挑战，研究人员开发了域的适应技术，这些技术转移了模型在整个领域中学到的知识。尽管现有的域适应技术已经改善了跨域的性能，但可以改进它们以适应现实世界中的实时数据提要情况，在该情况下，模型可以在部署时可以自动调整。在本文中，我们提出了一种基于强化的学习策略（RL-DA），用于在与环境互动并收集持续反馈的同时，将预训练的模型调整为现实世界中的实时数据供稿设置。 RL-DA对SER任务进行评估，包括跨语言和跨语言域的适应模式。评估结果表明，在实时数据供稿设置中，RL-DA的表现分别超过基线策略，分别在跨科普斯和跨语言场景中均优于11％和14％。
Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address this challenge, researchers have developed domain adaptation techniques that transfer knowledge learnt by a model across the domain. Although existing domain adaptation techniques have improved performances across domains, they can be improved to adapt to a real-world live data feed situation where a model can self-tune while deployed. In this paper, we present a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained model to a real-world live data feed setting while interacting with the environment and collecting continual feedback. RL-DA is evaluated on SER tasks, including cross-corpus and cross-language domain adaption schema. Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.
