评估低资源的随机森林表现 语音情感识别 评估随机森林表现
Muhammad Adeel1,2,3, Zhi-Yong Tao4,5, Shu-Ya Jin4,5
1Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin, 541004, People's Republic of China. adeel.muhammad@guet.edu.cn.
这项研究表明,随机森林分类器在乌尔都语语音情感识别 (SER) 中达到94.53%的准确性,识别幸福,悲伤和愤怒. 这促进了对低资源语言的同情AI的发展.
科学领域:
- 人与计算机的交互 (HCI)
- 人工智能 (AI) 是一种人工智能.
- 语音处理 语音处理
背景情况:
- 语音情感识别 (SER) 对HCI中的同情AI至关重要.
- 乌尔都语是一个资源较低的语言,SER研究有限.
- 现有的SER模型经常与语言多样性作斗争.
研究的目的:
- 为了评估随机森林 (RF) 分类器对SER的有效性.
- 为了确定关键的Mel频率 cepstral系数 (MFCCs) 用于情感歧视.
- 在资源不足的语言中推进AI的情感理解.
主要方法:
- 用于特征提取的Mel频率塞普斯特拉系数 (MFCCs).
- 使用随机森林 (RF) 分类器进行情感分类.
- 专注于三个主要情绪:快乐,悲伤和愤怒.
主要成果:
- 在乌尔都语SER中获得了94.53%的验证准确性.
- 证明了RF分类器对此任务的稳定性.
- 确定了有意义的MFCC特征,有助于情感差异化.
结论:
- 射频分类器是像乌尔都语这样的低资源语言中SER的可行和有效工具.
- 这项研究为更具情感智能的AI系统铺平了道路.
- 未来的工作包括扩大情感类别和探索各种数据集.
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