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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...
Speaker independent emotion recognition is particularly difficult for the individual differences of acoustic character and culture background. So, relative features obtained by calculating the features change of emotion speech relative to natural speech are adopted to weaken the influence from the individual differences in the paper. Moreover, an improved ranked voting fusion system is proposed to...
Lifelike agents, as a promising technology for human-computer interaction, have become focus of research community in resent years. In this paper, we will endow the lifelike agents with affective recognition capacity. There are three main contributions in this paper. Firstly, a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) is proposed to classify speech emotions. Secondly,...
Speech emotion recognition as a significant part has become a challenge to artificial emotion. It is particularly difficult to recognize emotion independent of the person concentrating on the speech channel. In the paper, an integrated system of hidden Markov model (HMM) and support vector machine (SVM), which combining advantages on capability to dynamic time warping of HMM and pattern recognition...
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