###### Abstract
To address the challenge of cross-domain TL in SER, this study explores the use of models from two diverse source domains, including state-of-the-art voice-based speaker embedding models (d-vector, x-vector, and r-vector), originally developed for speaker recognition tasks [13, 14, 15], as well as image classification models (AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and ResNet-50). Nowadays, image classification models, largely driven by deep learning, achieve impressive performance. Specifically, pre-trained architectures like ResNet are trained on large-scale image datasets, enabling them to extract rich and generalizable feature representations. These models are designed to identify patterns, edges, and textures, which can be analogous to the spectral features in audio data when transformed into spectrograms or other visual representations. The speaker embedding models simplify complex vocal features into compact manageable representations, in a way that captures unique characteristics of individual speakers. For instance, the x-vector model, developed by [PERSON] et al. [13], uses Time-delay Neural Networks (TDNNs) to create consistent representations of speech, while the r-vector, introduced by [PERSON] et al. [15], employs a CNN-based ResNet architecture to further enhance the model's compactness and discriminative power. By adapting these models for SER, we aim to transfer the knowledge gained to improve SER performance, particularly in identifying and interpreting emotional nuances in speech [16]. In addition, we examined our own enhanced versions of the conventional x-/r-vectors, that were developed in our previous work [17]. These enhanced embeddings incorporate architectural improvements such as Multi-Head Attention Pooling and Angular Margin Softmax, which are expected to better exploit the potential of the models for SER. In this study, emotion-rich datasets such as IEMOCAP and CREMA-D were used for training and evaluation of the downstream SER task. To address the challenge of data scarcity, a data augmentation technique employing reverberation was applied. The availability of diverse training data plays a critical role in the development of robust SER systems. The utilization of TL from image or speaker recognition models, along with the proposed enhancements and augmentation, is expected to lead to a more comprehensive modelling of emotional states in speech, promising improvements in the performance and robustness of SER systems. The main contributions of this study, compared to other recently published TL approaches, are as follows:  
* We applied TL from both speaker recognition and image classification to the SER task, utilizing d-vector, x-vector, and r-vector speaker embeddings, as well as image-based models like AlexNet, GoogLeNet, ResNet-18, and ResNet-50. * We demonstrated that speaker models pre-trained on broad and diverse speech datasets are also effective for accurately modelling speech emotions. * We refined the architectures of the x-vector and r-vector architectures with innovations such as Multi-Head Attention Pooling and Angular Margin Softmax to improve their ability to distinguish emotional states in speech. Additionally, incorporating data augmentation, particularly the addition of reverberation, could further enhance model generalization and reduce overfitting. * We showed that the proper design and selection of TL models significantly impact the efficiency and accuracy of SER systems. The structure of this paper is organized as follows: Section 2 presents a comprehensive review of related work in SER research. Section 3 describes the proposed methodology, detailing the architectures of the models utilized. Section 4 focuses on the databases and data augmentation techniques employed in the study, providing an explanation of the experimental setup, including the configuration of both image and speaker embedding models, as well as the evaluation metrics. Section 5 reports the experimental results and comparative analyses.