Speech emotion recognition with acoustic and lexical features

Qin Jin · Chengxin Li · Shizhe Chen · Huimin Wu

177 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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

Abstract

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2005Toward detecting emotions in spoken dialogs 2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2000Acoustical properties of speech as indicators of depression and suicidal risk 2004Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture 2012Speech emotion recognition: Features and classification models

Cited by

2020Multimodal Approach of Speech Emotion Recognition Using Multi-Level Multi-Head Fusion Attention-Based Recurrent Neural Network 2018Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment 2023A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods 2019Improving Speech Emotion Recognition with Unsupervised Representation Learning on Unlabeled Speech

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