Emotion detection in speech using deep networks

We propose a novel staged hybrid model for emotion detection in speech. Hybrid models exploit the strength of discriminative classifiers along with the representational power of generative models. Discriminative classifiers have been shown to achieve higher performances than the corresponding genera...

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Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3724 - 3728
Main Authors Amer, Mohamed R., Siddiquie, Behjat, Richey, Colleen, Divakaran, Ajay
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
CRBMs
CRF
Deep Networks
Emotion recognition
Feature extraction
Hidden Markov models
Hybrid Models
Hybrid power systems
Speech
Speech recognition
Vectors
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Summary:We propose a novel staged hybrid model for emotion detection in speech. Hybrid models exploit the strength of discriminative classifiers along with the representational power of generative models. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative representations. Our proposed hybrid model consists of a generative model, which is used for unsupervised representation learning of short term temporal phenomena and a discriminative model, which is used for event detection and classification of long range temporal dynamics. We evaluate our approach on multiple audio-visual datasets (AVEC, VAM, and SPD) and demonstrate its superiority compared to the state-of-the-art.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854297