标题： 绘制深度学习在语音情感识别中15年进展的图表：一项复制研究 显示英文标题

标题： Charting 15 years of progress in deep learning for speech emotion recognition: A replication study

摘要： 语音情感识别(SER)长期以来受益于深度学习方法的应用。 更深的模型——具有更多层和更多可训练参数——通常被SER社区认为是“更好”的。 这引发了一个问题——\emph{更好多少}现代时代的深度神经网络与早期版本相比如何？ 除此之外，如何前进的更重要问题依然同样紧迫。 SER远未解决；因此，确定未来研究最显著的途径至关重要。 在本次贡献中，我们尝试量化自2009年INTERSPEECH情感挑战赛这一里程碑引入以来15年研究中的进展。 我们对模型架构进行了大规模调查，涵盖了依赖语音输入的基于音频的模型和仅依赖转录文本的基于文本的模型。 我们的结果表明，在最近引入Transformer架构之后，收益递减并趋于平稳。 此外，我们展示了对进展的感知是如何取决于所比较模型的具体选择的。 我们的发现对SER研究的最新状态和前进路径有重要的影响。 显示英文摘要

摘要： Speech emotion recognition (SER) has long benefited from the adoption of deep learning methodologies. Deeper models -- with more layers and more trainable parameters -- are generally perceived as being `better' by the SER community. This raises the question -- \emph{how much better} are modern-era deep neural networks compared to their earlier iterations? Beyond that, the more important question of how to move forward remains as poignant as ever. SER is far from a solved problem; therefore, identifying the most prominent avenues of future research is of paramount importance. In the present contribution, we attempt a quantification of progress in the 15 years of research beginning with the introduction of the landmark 2009 INTERSPEECH Emotion Challenge. We conduct a large scale investigation of model architectures, spanning both audio-based models that rely on speech inputs and text-baed models that rely solely on transcriptions. Our results point towards diminishing returns and a plateau after the recent introduction of transformer architectures. Moreover, we demonstrate how perceptions of progress are conditioned on the particular selection of models that are compared. Our findings have important repercussions about the state-of-the-art in SER research and the paths forward