论文标题
通过跨科语语音数据集的域改编对意大利老年人的情感识别
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset
论文作者
论文摘要
这项工作的目的是定义语音情感识别（SER）模型，能够在意大利老年人的自然对话中识别积极，中立和负面情绪。文献中有几个用于SER的数据集。然而，其中大多数是用英语或中文的，在演员和女演员发音简短的短语中已被记录，因此与自然对话无关。此外，所有数据库中只有很少的演讲与老年人有关。因此，在这项工作中，多语言和多年龄语料库被认为是用英语合并数据集的，其中还包括老年人，以及意大利语中的数据集。基于XGBoost，提出了一种对年轻和成人英国演员进行培训的通用模型。然后提出了两种域名适应的策略，以适应老年人和意大利语者的模型。结果表明，这种方法提高了分类性能，还强调了应收集新数据集。
The aim of this work is to define a speech emotion recognition (SER) model able to recognize positive, neutral and negative emotions in natural conversations of Italian elderly people. Several datasets for SER are available in the literature. However most of them are in English or Chinese, have been recorded while actors and actresses pronounce short phrases and thus are not related to natural conversation. Moreover only few speeches among all the databases are related to elderly people. Therefore, in this work, a multi-language and multi-age corpus is considered merging a dataset in English, that includes also elderly people, with a dataset in Italian. A general model, trained on young and adult English actors and actresses is proposed, based on XGBoost. Then two strategies of domain adaptation are proposed to adapt the model either to elderly people and to Italian speakers. The results suggest that this approach increases the classification performance, underlining also that new datasets should be collected.
