Dialects, in their remarkable diversity, serve as repositories of rich historical and cultural heritage. The Guanzhong dialect, native to the Guanzhong region of Shaanxi, is characterized by distinct phonetics, vocabulary, and cultural nuances. With the rapid advancement of speech emotion recognition (SER) technology, research into emotion analysis for dialectal speech has gained traction. However, this field remains challenged by limited dialect-specific datasets, an inadequate representation of emotional features in speech, and subpar model performance. The core objective of research is to achieve an accurate recognition of emotions in dialect environments, overcome the limitations of scarce dialect data, and meet the emotional interaction needs of specific groups of people. This work presents a dual contribution: first, there is the construction of a high-fidelity Guanzhong dialect SER dataset through systematic data collection, noise reduction preprocessing, and the annotation of four emotional categories (joy, sadness, anger, neutral), which establishes a robust foundation for subsequent research. Second, there is the development of FCAL-Net, which is a novel framework integrating multi-dimensional feature fusion. This approach leverages the CAM (convolutional attention module) to enhance CNN's local temporal feature extraction, couples it with Bi-LSTM for global contextual modeling, and demonstrates superior performance. The experimental results validate an accuracy of 77.67% in emotion classification, outperforming traditional CNN (12.31% improvement), Bi-LSTM (7.23%), Wav2vec 2.0 (8.08%), and Conformer (4.61%) baselines-highlighting its efficacy in addressing core challenges in dialectal SER.
Zhang et al. (Wed,) studied this question.