摘要：Based on the large medical data to evaluate the performance of the hearing aid is a promising way. Achieving the classification of the hearing aid is the foundation. In this paper an improved semisupervised AP clustering algorithm based on density path is proposed. The PESQ score is taken as the substitution of subjective score for the speech segments, which is also taken as a semi-supervised basis to improve classification accuracy. The Euclidean distance similarity is improved based on the density path, making it suitable for complex shape data sets. Through experimental verification, compared with the traditional AP algorithm, the improved algorithm shows obvious advantages in terms of hearing aid classification accuracy and recognition performance.
