Speech Emotion Recognition (SER) plays a crucial role in advancing human-computer interaction and speech processing capabilities. We introduce a novel deep-learning architecture designed specifically for the functional data model known as the multiple-index functional model. Our key innovation lies in integrating adaptive basis layers and an automated data transformation search within the deep learning framework. Simulations for this new model show good performances. This allows us to extract features tailored for chunk-level SER, based on Mel Frequency Cepstral Coefficients (MFCCs). We demonstrate the effectiveness of our approach on the benchmark IEMOCAP database, achieving good performance compared to existing methods.

中文翻译：

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深度函数多索引模型及其在 SER 中的应用

语音情感识别（SER）在提高人机交互和语音处理能力方面发挥着至关重要的作用。我们引入了一种专门为函数数据模型设计的新颖的深度学习架构，称为多索引函数模型。我们的关键创新在于在深度学习框架内集成自适应基础层和自动数据转换搜索。这个新模型的模拟显示出良好的性能。这使我们能够基于梅尔频率倒谱系数 (MFCC) 提取针对块级 SER 定制的特征。我们在基准 IEMOCAP 数据库上证明了我们方法的有效性，与现有方法相比，取得了良好的性能。