###### Abstract  
This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation. spoken English; large language models (LLMs); emotion recognition; emotion evaluation; emotional features +
Footnote †: journal: Medicalachievements. They often openly express their confidence and honor. Westerners have independent views on themselves and possess relative independence. Taking conversations between Asians and Westerners as an example, cultural differences may lead to significant variations in the expression and interpretation of the same emotions. In Western culture, individuals tend to express their emotions and feelings directly, while in Asian culture, people may be more reserved and indirect. Such differences can lead to misunderstandings and communication barriers. Therefore, incorporating emotional evaluation into the system is of great significance. It helps Chinese students to improve their ability to express emotions in the English language and enhances the evaluation system. Currently, the discrete emotional model is primarily adopted to describe vocal emotions as independent labels without interrelation, among which the American psychologist [PERSON]'s classification of six basic emotions--anger, disgust, fear, happiness, sadness, and surprise--is widely used in the current field of emotion-related research [4]. Speech emotion recognition technology is the process of extracting feature parameters related to emotional representation from speech signals and establishing a mapping model between these feature parameters and emotional categories. Its core goal is to achieve the emotional classification of different speech samples. Despite the significant progress achieved in the field of emotion classification [5; 6; 7; 8], existing research primarily focuses on classification with limited attention to the quantitative evaluation of emotions, especially in the application of spoken English learning assessments. To objectively and automatically assess the emotional expression in students' spoken English, this study meticulously constructs a specialized dataset for the evaluation of emotions in spoken English. This is achieved by leveraging existing public emotion datasets (such as IEMOCAP [9]), self-generated emotional speech materials utilizing the large language model (LLM) strategy, and students' emotional speech audio captured by ourselves. Based on the attention-based transformer model, emotional features are successfully decoupled from other speech characteristics like speech rate and intonation in English spoken audio.