Modeling the Temporal Evolution of Acoustic Parameters for Speech Emotion Recognition

Stavros Ntalampiras · Nikos Fakotakis

89 citations0/0 files storedIEEE Transactions on Affective Computing

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

Abstract

During recent years, the field of emotional content analysis of speech signals has been gaining a lot of attention and several frameworks have been constructed by different researchers for recognition of human emotions in spoken utterances. This paper describes a series of exhaustive experiments which demonstrate the feasibility of recognizing human emotional states via integrating low level descriptors. Our aim is to investigate three different methodologies for integrating subsequent feature values. More specifically, we used the following methods: 1) short-term statistics, 2) spectral moments, and 3) autoregressive models. Additionally, we employed a newly introduced group of parameters which is based on the wavelet decomposition. These are compared with a baseline set comprised of descriptors which are usually used for the specific task. Subsequently, we experimented on fusing these sets on the feature and log-likelihood levels. The classification step is based on hidden Markov models, while several algorithms which can handle redundant information were used during fusion. We report results on the well-known and freely available database BERLIN using data of six emotional states. Our experiments show the importance of including information which is captured by the set based on multiresolution analysis and the efficacy of merging subsequent feature values.

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References

2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2005Toward detecting emotions in spoken dialogs 2001Nonlinear feature based classification of speech under stress 2003Modeling drivers’ speech under stress 2007Speech Emotion Recognition using Gaussian Mixture Vector Autoregressive Models 2007Frame vs. Turn-Level: Emotion Recognition from Speech Considering Static and Dynamic Processing

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