Emotion recognition is a critical task in human-computer interaction, enabling more intuitive and responsive systems. This study presents a multimodal emotion recognition system that combines low-level information from audio and text, leveraging both Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory Networks (BiLSTMs). The proposed system consists of two parallel networks: an Audio Block and a Text Block. Mel Frequency Cepstral Coefficients (MFCCs) are extracted and processed by a BiLSTM network and a 2D convolutional network to capture low-level intrinsic and extrinsic features from speech. Simultaneously, a combined BiLSTM-CNN network extracts the low-level sequential nature of text from word embeddings corresponding to the available audio. This low-level information from speech and text is then concatenated and processed by several fully connected layers to classify the speech emotion. Experimental results demonstrate that the proposed EmoTech accurately recognizes emotions from combined audio and text inputs, achieving an overall accuracy of 84%. This solution outperforms previously proposed approaches for the same dataset and modalities.

翻译：情感识别是人机交互中的关键任务，能够实现更直观、响应更迅速的系统。本研究提出一种多模态情感识别系统，该系统结合来自音频和文本的低层信息，并利用卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）。所提出的系统包含两个并行网络：音频模块和文本模块。梅尔频率倒谱系数（MFCC）被提取出来，并通过一个BiLSTM网络和一个二维卷积网络进行处理，以从语音中捕获低层内在和外在特征。同时，一个组合的BiLSTM-CNN网络从与可用音频对应的词嵌入中提取文本的低层序列特性。来自语音和文本的这些低层信息随后被拼接，并由多个全连接层处理，以对语音情感进行分类。实验结果表明，所提出的EmoTech能够准确识别来自音频和文本组合输入的情感，总体准确率达到84%。该解决方案优于先前针对相同数据集和模态提出的方法。