Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks

Duc Le · Emily Mower Provost

119 citations0/0 files storedIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Research in emotion recognition seeks to develop insights into the temporal properties of emotion. However, automatic emotion recognition from spontaneous speech is challenging due to non-ideal recording conditions and highly ambiguous ground truth labels. Further, emotion recognition systems typically work with noisy high-dimensional data, rendering it difficult to find representative features and train an effective classifier. We tackle this problem by using Deep Belief Networks, which can model complex and non-linear high-level relationships between low-level features. We propose and evaluate a suite of hybrid classifiers based on Hidden Markov Models and Deep Belief Networks. We achieve state-of-the-art results on FAU Aibo, a benchmark dataset in emotion recognition [1]. Our work provides insights into important similarities and differences between speech and emotion.

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References

2009The INTERSPEECH 2009 emotion challenge 2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2013Deep learning for robust feature generation in audiovisual emotion recognition 2011Deep neural networks for acoustic emotion recognition: Raising the benchmarks

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2018A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression 2020Emotion recognition using speech and neural structured learning to facilitate edge intelligence 2019Learning Temporal Clusters Using Capsule Routing for Speech Emotion Recognition 2018Speech emotion recognition research: an analysis of research focus 2015Speech emotion recognition in emotional feedback for Human-Robot Interaction 2016Unsupervised domain adaptation for speech emotion recognition using PCANet 2018Effective Attention Mechanism in Dynamic Models for Speech Emotion Recognition 2022Hybrid deep learning with optimal feature selection for speech emotion recognition using improved meta-heuristic algorithm

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