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首页> 外文期刊>International journal of advanced pervasive and ubiquitous computing >Infant Cry Recognition System: A Comparison of System Performance based on CDHMM and ANN
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Infant Cry Recognition System: A Comparison of System Performance based on CDHMM and ANN

机译：婴儿哭识别系统：基于CDHMM和ANN的系统性能比较

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摘要

Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.
机译：婴儿的呼喊可以被视为痛苦的指标。已经证明，痛苦，饥饿，恐惧，压力等引起的哭泣，显示出不同的哭泣模式。这里提出的工作介绍了在自动分类系统中实现的两种不同分类技术的性能之间的比较研究，用于鉴定两种类型的婴儿哭泣，疼痛和非疼痛。该技术是即连续隐马尔可夫模型（CHMM）和人工神经网络（ANN）。从响铃样本中提取了两组不同的声学特征，其中是MFCC和LPCC，每个特征向量最终被馈送到分类模块中，以便训练和测试。这项工作的结果表明，基于CDHMM的系统比基于ANN的性能更好。 CDHMM为96.1％提供最佳识别率，高于79％的ANN，一般来说，基于MFCC功能的系统比利用LPCC功能更好。

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