Teacher-to-Teacher: Harmonizing Dual Expertise into a Unified Speech Emotion Model

This paper introduces the Teacher-to-Teacher (T2T) framework, a novel approach in speech emotion recognition (SER) specifically tailored for the Thai language. Leveraging the dual expertise of the Wav2Vec and Wav2Vec2 models, the T2T framework utilizes unsupervised and self-supervised learning knowledges to effectively address the unique challenges posed by tonal languages. By integrating these two powerful models into a unified SER framework, T2T enhances its capability to process and interpret nuanced emotional cues in speech, achieving superior performance compared to traditional SER methods. Evaluated across three major datasets—ThaiSER, EMOLA, and MU—the framework demonstrates significant improvements in unweighted accuracy and F1-score. Innovations such as emotional clustering representation and targeted emotional representation contribute to its high precision in detecting and differentiating subtle emotional states. Additionally, the integration of a fine-tuned teacher module aligns these advancements with practical SER applications, further increasing the framework's accuracy and sensitivity in real-world scenarios. The successful implementation of the T2T framework opens new avenues for enhancing SER technologies in other low-resource languages and extends its applicability to real-time processing applications, thereby advancing the field of computational emotion recognition.