Open Access
Speech emotion recognition based on a hybrid of HMM/ANN
Xia Mao,Bing Zhang,Yi Luo +2 more
- pp 367-370
TLDR
A hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) has been proposed to classify emotions, combining advantage on capability to dynamic time warping of HMM and pattern recognition of ANN.Abstract:
Speech emotion recognition, as a vital part of affective human computer interaction, has become a new challenge to speech processing. In this paper, a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) has been proposed to classify emotions, combining advantage on capability to dynamic time warping of HMM and pattern recognition of ANN. HMMs, which export likelihood probabilities and optimal state sequences, have been used to model speech feature sequences, while ANN has been employed to make a decision. The recognition result of the hybrid classification has been compared with the isolated HMMs by two speech corpora, Germany database and Mandarin database, and the average recognition rates have reached 83.8% and 81.6% respectively.read more
Citations
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Journal ArticleDOI
Speech emotion recognition: Features and classification models
TL;DR: The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition.
Journal ArticleDOI
Emotion detection from text and speech: a survey
TL;DR: Existing emotion detection research efforts, emotion models, emotion datasets, emotion detection techniques, their features, limitations and some possible future directions are reviewed, focusing on reviewing research efforts analyzing emotions based on text and speech.
Proceedings ArticleDOI
Speech emotion recognition using Support Vector Machines
TL;DR: An attempt has been made to recognize and classify the speech emotion from three language databases, namely, Berlin, Japan and Thai emotion databases, using Support Vector Machines (SVM) as the classification model.
Proceedings ArticleDOI
Implementing emotion-based user-aware e-learning
TL;DR: An intelligent e-learning system featuring with affective agent tutor "Alice" fully capable of adapting to these states wisely guided by a case-based method with facial expression generation and emotional speech synthesis ability.
Journal ArticleDOI
Speech emotion recognition research: an analysis of research focus
TL;DR: Analysis of research in speech emotion recognition from 2006 to 2017 finds that certain combination of databases, speech features and classifiers influence the recognition accuracy of the SER system.
References
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TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Proceedings ArticleDOI
Hidden Markov model-based speech emotion recognition
TL;DR: The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives and describes a speech corpus consisting of acted and spontaneous emotion samples in German and English language.
Proceedings ArticleDOI
Speech emotion recognition based on HMM and SVM
Yi-Lin Lin,Gang Wei +1 more
TL;DR: Two classification methods, the hidden Markov model (HMM) and the support vector machine (SVM), are used, to classify five emotional states: anger, happiness, sadness, surprise and a neutral state.
Proceedings ArticleDOI
Automatic statistical analysis of the signal and prosodic signs of emotion in speech
Roddy Cowie,Ellen Douglas-Cowie +1 more
TL;DR: The authors highlight two broader domains surrounding specific attributions of emotion and the specific features of speech that underlie them, and argue for caution over compartmentalising these, broader domains.
Proceedings ArticleDOI
Speech emotion classification with the combination of statistic features and temporal features
Dan-Ning Jiang,Lianhong Cai +1 more
TL;DR: Experiments on a Chinese speech corpus have demonstrated that the proposed classification scheme could improve the classification accuracy greatly, and detailed analysis indicated that these two feature representations could compensate each other efficiently in the classification.