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Data selection is an important component of cross-corpus training and semi-supervised/active learning. However, its effect on acoustic emotion recognition is still not well understood. In this work, we perform an in-depth exploration of various data selection strategies for emotion classification from speech using classifier agreement as the selection metric. Our methods span both the traditional...
According to multidimensional emotion space model, an improved queuing voting algorithm was proposed to implement the fusion among multiple emotion classifiers for a good emotion recognition result. Firstly, three kinds of classifier were designed based on hidden Markov model (HMM) and artificial neural network (ANN). Then, the improved queuing voting algorithm was used to fuse them. Experimental...
This paper constructs a VQ/ANN (vector quantization/artificial neural network) based speech emotion recognition system. The system first extracts the basic prosodic parameters and Mel-frequency cepstral coefficients (MFCC) frame by frame. Recent researches reveal that MFCC convey detailed emotional relevant information of syllable. However, the statistic measures of MFCC confuse the information at...
This paper proposes a new approach for emotion recognition based on a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN), using both utterance and segment level information from speech. To combine the advantage on capability to dynamic time warping of HMMs and pattern recognition of ANN, the utterance is viewed as a series of voiced segments, and feature vectors extracted from...
Artificial neural network (ANN) models based on static features vector as well as normalized temporal features vector, were used to recognize emotion state from speech. Moreover, relative features obtained by computing the changes of acoustic features of emotional speech relative to those of neutral speech were adopted to weaken the influence from the individual difference. The methods to relativize...
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