# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
## 1 Introduction  
In the contemporary era, the exponential proliferation of multimedia content has led to a precipitously rising interest in the domain of multimodal data interpretation among academic scholars. This discipline encompasses the evaluation of data through diverse channels, such as visual and textual streams [1, 2, 3]. A pivotal and emergent subfield in this realm is the study of multimodal emotional analysis, which involves deciphering human sentiments as manifested across diverse communication modalities. This avenue of innovation has witnessed escalating momentum within the scholarly domain over the past few years. Multimodal frameworks endorse an interactive paradigm that emulates human dialogue by permitting the concurrent utilization of multiple data ingestion and emission channels. The liberty to opt for an array of these modalities cultivates a more anthropomorphic dialogue experience, enhancing user immersion and communicationefficacy. A significant body of scholarly work has been dedicated to exploring sentiment discernment in the written content [4; 5], facial affective interpretation [6; 7], and auditory emotional cognition [8; 9]. Nevertheless, scrutiny of these outcomes indicates that inquiries centered on singular modes have encountered a particular impasse, prompting amplified scholarly interest in the utilization of multimodal methodology. Amalgamating ancillary data from pictorial and textual sources can augment the fidelity of affective discernment, and thus foster the evolution of machines equipped with empathic faculties. The domain of multimodal affective identification encompasses an extensive array of implementations such as human-machine interfacing, automated detection systems, intelligent playthings, protective mechanisms, service dispensation, and linguistic transposition. Within the realm of multimodal emotion recognition, the paramount phases encompass the derivation of distinguishing attributes and the amalgamation of these elements across diverse modalities. The objective of attribute derivation is to discern the vital constituents of signals, formulate vector representations predicated on these constituents, and employ them to classify pertinent emotions. This process streamlines the subsequent phases of the emotion discernment task [10]. Prior investigations typically derived features intrinsic to each modality, such as lexical representations from textual data [11], temporal acoustic attributes from vocal utterances [12], and visual cues from facial imagery [13]. Existing extraction methodologies include techniques such as Fourier transformation, wavelet transformation, and high-order cross processes, which can be collectively categorized as low-level feature extraction methods. However, given the dichotomy between human affective states and low-level attributes, these extraction strategies may not be adequately robust for multimodal emotion identification. Conversely, deep learning methodologies can detect the dispersion attributes of datasets by integrating basal characteristics, thereby constructing a more abstract, superior-level portrayal of information. Furthermore, multimodal amalgamation, which entails merging data obtained from various channels, profoundly affects the efficiency and outcomes of multimodal emotional discern [14; 15]. This amalgamation procedure provides supplementary data, consequently improving the fidelity of emotional identification. Therefore, conceptualizing a more advanced fusion strategy is imperative to yield a superior optimized discriminative feature representation for multimodal emotion recognition. Emotional expressions often manifest as distinct energy patterns in spectrograms, particularly in auditory formats like speech. These patterns display specific distributions of energy across different frequencies, making them identifiable when analyzed through spectrograms. Convolutional neural networks (CNNs) are particularly adept at emphasizing areas in the data that exhibit high energy levels or sudden transitions.