arXiv cs.AI· Bahman Mirheidari, Yilin Pan, Daniel Blackburn, Ronan O'Malley, Traci Walker, Annalena Venneri, Markus Reuber, Heidi Christensen·· 3 小时前AI 评分29
用生成网络做数据增强以识别痴呆症
Data augmentation using generative networks to identify dementia
AI 导读
研究者将生成模型数据增强应用于自动痴呆症检测系统的语音与音频特征，用两个生成模型合成样本改进 DNN 分类器。变分自编码器将区分记忆门诊典型患者群体的四分类 F-score 从 58% 提升至约 74%，提高 16 个百分点。
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