面向跨语料库语音情感识别的层自适应隐式分布对齐网络
Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
AI总结：
针对跨语料库语音情感识别的语料差异难题，提出层自适应隐式分布对齐网络LIDAN，通过分层设置不同粒度的隐式分布对齐项学习鲁棒特征，在三个语料库上性能超越现有显式无监督域自适应方法。
摘要：
本文提出了一种新的无监督域自适应（DA）方法，名为层自适应隐式分布对齐网络（LIDAN），以解决跨语料库语音情感识别（SER）的挑战。LIDAN是对我们此前发表于ICASSP的工作——深度隐式分布对齐网络（DIDAN）的扩展，DIDAN的核心贡献在于提出了一种名为隐式分布对齐（IDA）的新型正则化项。该正则化项使得在源（训练）语音样本上训练的DIDAN，即便存在跨语料库SER中的语料库差异，仍可用于预测目标（测试）语音样本的情感标签。为进一步提升该方法性能，我们将IDA扩展为层自适应IDA（LIDA），由此得到LIDAN。这一层自适应扩展包含三个改进的IDA项，它们分别考虑不同粒度的情感标签。这些项被策略性地设置在LIDAN的不同全连接层中，与随网络深度递增的情感判别能力相匹配。这种排布使得LIDAN相较于DIDAN，能够更有效地学习具备情感判别性且语料库不变的特征，用于跨不同语料库的SER任务。值得一提的是，现有大多数方法依赖估计统计矩来描述预先假设的显式分布，而IDA和LIDA采用了不同的思路。它们利用目标样本重构的思想，直接缩小特征分布差距，无需对分布类型做出假设。因此，DIDAN和LIDAN可被视为隐式跨语料库SER方法。为评估LIDAN的性能，我们在EmoDB、eNTERFACE和CASIA三个语料库上开展了大量跨语料库SER实验。实验结果表明，在处理跨语料库SER任务方面，LIDAN的表现优于当前最新的显式无监督DA方法。
英文摘要：
In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDAN), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This term allows DIDAN trained on source (training) speech samples to remain applicable to predicting emotion labels for target (testing) speech samples, regardless of corpus variance in cross-corpus SER. To further enhance this method, we extend IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated extention consists of three modified IDA terms that consider emotion labels at different levels of granularity. These terms are strategically arranged within different fully connected layers in LIDAN, aligning with the increasing emotion-discriminative abilities with respect to the layer depth. This arrangement enables LIDAN to more effectively learn emotion-discriminative and corpus-invariant features for SER across various corpora compared to DIDAN. It is also worthy to mention that unlike most existing methods that rely on estimating statistical moments to describe pre-assumed explicit distributions, both IDA and LIDA take a different approach. They utilize an idea of target sample reconstruction to directly bridge the feature distribution gap without making assumptions about their distribution type. As a result, DIDAN and LIDAN can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA corpora. The experimental results demonstrate that LIDAN surpasses recent state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
