Speech emotion recognition is crucial to human-computer interaction. The
temporal regions that represent different emotions scatter in different parts
of the speech locally. Moreover, the temporal scales of important information
may vary over a large range within and across speech segments. Although
transformer-based models have made progress in this field, the existing models
could not precisely locate important regions at different temporal scales. To
address the issue, we propose Dynamic Window transFormer (DWFormer), a new
architecture that leverages temporal importance by dynamically splitting
samples into windows. Self-attention mechanism is applied within windows for
capturing temporal important information locally in a fine-grained way.
Cross-window information interaction is also taken into account for global
communication. DWFormer is evaluated on both the IEMOCAP and the MELD datasets.
Experimental results show that the proposed model achieves better performance
than the previous state-of-the-art methods.

本研究提出了一种基于动态窗口的变压器架构 DWFormer，通过自注意机制动态划分样本窗口，捕捉语音本地的精细的时间重要信息，并考虑窗口间的全局信息交互，实现了在情感识别方面的前沿突破。

DWFormer: 动态窗口变换器用于语音情感识别

DWFormer: Dynamic Window transFormer for Speech Emotion Recognition

We develop a generalized method of moments (GMM) approach for fast parameter
estimation in a new class of Dirichlet latent variable models with mixed data
types. Parameter estimation via GMM has been demonstrated to have computational
and statistical advantages over alternative methods, such as expectation
maximization, variational inference, and Markov chain Monte Carlo. The key
computational advan- tage of our method (MELD) is that parameter estimation
does not require instantiation of the latent variables. Moreover, a
representational advantage of the GMM approach is that the behavior of the
model is agnostic to distributional assumptions of the observations. We derive
population moment conditions after marginalizing out the sample-specific
Dirichlet latent variables. The moment conditions only depend on component mean
parameters. We illustrate the utility of our approach on simulated data,
comparing results from MELD to alternative methods, and we show the promise of
our approach through the application of MELD to several data sets.

本文提出了一种基于广义矩 (MOM) 的快速参数估计方法，可以在混合数据类型的 Dirichlet 潜变量模型中应用，该方法不需要潜变量的实例化，有效地解决了参数估计的计算和统计问题。