Generative Adversarial Network-Based Neural Audio Caption Model for Oral Evaluation

• Main Authors: Liu Zhang, Chao Shu, Jin Guo, Hanyi Zhang, Cheng Xie, Qing Liu
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Electronics
• Subjects: oral evaluation; generative adversarial network; neural audio caption; gated recurrent unit; long short-term memory
• Online Access: https://www.mdpi.com/2079-9292/9/3/424

Oral evaluation is one of the most critical processes in children’s language learning. Traditionally, the Scoring Rubric is widely used in oral evaluation for providing a ranking score by assessing word accuracy, phoneme accuracy, fluency, and accent position of a tester. In recent years, by the emerging demands of the market, oral evaluation requires not only providing a single score from pronunciation but also in-depth, meaning comments based on content, context, logic, and understanding. However, the Scoring Rubric requires massive human work (oral evaluation experts) to provide such deep meaning comments. It is considered uneconomical and inefficient in the current market. Therefore, this paper proposes an automated expert comment generation approach for oral evaluation. The approach first extracts the oral features from the children’s audio as well as the text features from the corresponding expert comments. Then, a Gated Recurrent Unit (GRU) is applied to encode the oral features into the model. Afterwards, a Long Short-Term Memory (LSTM) model is applied to train the mappings between oral features and text features and generate expert comments for the new coming oral audio. Finally, a Generative Adversarial Network (GAN) is combined to improve the quality of the generated comments. It generates pseudo-comments to train the discriminator to recognize the human-like comments. The proposed approach is evaluated in a real-world audio dataset (children oral audio) collected by our collaborative company. The proposed approach is also integrated into a commercial application to generate expert comments for children’s oral evaluation. The experimental results and the lessons learned from real-world applications show that the proposed approach is effective for providing meaningful comments for oral evaluation.