基于感知滤波和神经网络的语音情感识别

R. A, S. N.
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引用次数: 0

摘要

本章关于多说话人独立的情绪识别,包括使用以等效矩形带宽(ERB)和BARK尺度间隔的滤波器的感知特征和向量量化(VQ)分类器对群体进行分类,以及使用反向传播算法的人工神经网络对群体进行情绪分类。通过使用相关情绪中的大量数据来充分训练系统,可以提高性能。在有限的数据集下,该系统通过ERB尺度的临界频带分析,为感知特征提供了一致的更好的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotion Recognition From Speech Using Perceptual Filter and Neural Network
This chapter on multi speaker independent emotion recognition encompasses the use of perceptual features with filters spaced in Equivalent rectangular bandwidth (ERB) and BARK scale and vector quantization (VQ) classifier for classifying groups and artificial neural network with back propagation algorithm for emotion classification in a group. Performance can be improved by using the large amount of data in a pertinent emotion to adequately train the system. With the limited set of data, this proposed system has provided consistently better accuracy for the perceptual feature with critical band analysis done in ERB scale.
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