This paper describes a novel call recognizer system based on the machine learning approach. Current trends, intelligence, emotional recognition and other factors are important challenges in the real world. The proposed system provides robustness with high accuracy and adequate response time for the human–computer interaction. Intelligence and emotion recognition from the speech of human–computer interfaces are simulated via multiple classifier systems (MCSs). At a higher-level stage, the acoustic stream phase extracts certain acoustic features based on the pitch and energy of the signal. Here, the feature space is labeled with various emotional types in the training phase. Emotional categories are trained in the acoustic feature space. The semantic stream process converts speech into text in the input speech signal. Text classification algorithms are applied subsequently. The clustering and classification process is performed via a K-means algorithm. The detection of the Tone of Voice of call recognition system is achieved with the XGBoost model for feature extraction and detection of a particular phrase in the client call phase. Speech expressions are used for understanding the human emotion. The algorithms are tested and demonstrate good performance in the simulation environment.

中文翻译：

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一种使用基于机器学习的方法的智能语音和呼叫识别系统的可执行方法

本文介绍了一种新的呼叫识别系统，基于机器学习方法。当前的趋势、智力、情感识别和其他因素是现实世界中的重要挑战。所提出的系统为人机交互提供了高精度和足够响应时间的鲁棒性。通过多分类器系统（MCS）模拟人机界面语音的智能和情感识别。在更高级别的阶段，声流相位根据信号的音高和能量提取某些声学特征。在这里，特征空间在训练阶段被标记为各种情绪类型。情感类别在声学特征空间中进行训练。语义流处理将输入语音信号中的语音转换为文本。随后应用文本分类算法。ķ-意味着算法。呼叫识别系统的音调检测是通过XGBoost模型实现的，用于在客户端呼叫阶段对特定短语进行特征提取和检测。语音表达用于理解人类的情感。这些算法在仿真环境中经过测试并展示了良好的性能。