Evaluation Metrics

1. Accuracy

Accuracy refers to the average correctness of the model across all evaluation instances. The concept of correctness may vary in different contexts, so we enumerate the main accuracy metrics considered in the evaluation work, the application scenarios of these metrics, and their formal definitions.

1.1 accuracy

The ratio of correctly predicted classifications to the total number of predictions.

2. Robustness

The designed dataset contains certain errors and noise, such as repetitions, hesitations, corrections, meaningless syllables, environmental noise, etc., to measure the model's accuracy on such data (approximately 10%).