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首页> 外文会议>International Conference on Advances in Big Data, Computing and Data Communication Systems >The Automatic Recognition of Sepedi Speech Emotions Based on Machine Learning Algorithms
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The Automatic Recognition of Sepedi Speech Emotions Based on Machine Learning Algorithms

机译：基于机器学习算法的Sepedi语音情绪自动识别

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Over the past years, speech emotion recognition (SER) studies have been gaining much interest in the fields of affective computing and human-computer interaction (HCI). The idea was to improve the interaction between human beings and machines. In this paper, an SER system that classifies and recognise six basic emotions (anger, sadness, disgust, fear, happiness, and neutral) from speech spoken in Sepedi language (one of South Africa's official languages) is discussed. Speech recordings were collected from the Sepedi language speakers and TV drama broadcast to create emotional speech corpora. 34 speech features were then extracted from the speech corpora, using the pyAudioAnalysis tool, to train and compare different algorithms using 10 folds cross-validation. The experiments were conducted using WEKA data-mining software. The results showed that Auto-WEKA outperforms all the standard algorithms (SVM. KNN and MLP). Recorded speech corpus yielded good recognition accuracy compare to TV broadcast speech corpus.
机译：在过去几年中，语音情感认知（SER）研究一直在对情感计算和人机交互（HCI）的领域获得了很多兴趣。这个想法是改善人类和机器之间的相互作用。在本文中，讨论了一名综合体系，分类和认识到六种基本情绪（南非南非官方语言之一）的讲话中的六种基本情绪（愤怒，悲伤，厌恶，幸福，幸福和中立）。从Sepedi语言发言者和电视剧广播中收集了语音录制，以创造情绪语音集团。然后，使用PyaudioAnalysis工具从语音语料库中提取34语音功能，以使用10倍交叉验证训练和比较不同的算法。使用Weka数据挖掘软件进行实验。结果表明，自动WEKA优于所有标准算法（SVM。KNN和MLP）。记录的语音语料库产生良好的识别准确性与电视广播语音语料库相比。

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