arXiv cs.AI· Bahman Mirheidari, Yilin Pan, Daniel Blackburn, Ronan O'Malley, Traci Walker, Annalena Venneri, Markus Reuber, Heidi Christensen·· 10 小时前AI 评分22
利用生成网络进行数据增强以识别痴呆症
Data augmentation using generative networks to identify dementia
AI 导读
研究用生成模型对自动痴呆检测系统记录的语音与音频特征做数据增强，以缓解医疗场景训练数据不足的问题。变分自编码器将区分记忆门诊常见患者组的四分类器 F-score 从 58% 提升至约 74%，提升 16%。
正文 · 原文
Abstract:Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2004.05989 [eess.AS] |
| (or arXiv:2004.05989v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2004.05989 arXiv-issued DOI via DataCite |
Submission history
From: Bahman Mirheidari [view email]
[v1]
Mon, 13 Apr 2020 15:05:24 UTC (438 KB)
来源：arXiv cs.AI · arxiv.org