Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

Björn Wolfgang Schuller · Lorenz Mösenlechner · Dejan Arsić · Gerhard Rigoll

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

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

Abstract

While the " 'quasi-state-of-the-art'" towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functionals as moments or extrema, only few respect statistical noise by outliers due to too long segments as turns. Such noise can be overcome by hierarchical functionals as means of extrema over smaller units as words or chunks. Segmentation of such units however usually relies on transcription. We therefore discuss hierarchical functionals based on automatic segmentation and their systematic generation as opposed to common expert-driven selection. To cope with rapidly growing feature spaces iquest5k, we discuss data-driven two-stage compression based on SVM- SFFS. Extensive test-runs are carried out on two known emotion and behavior corpora, and show superiority of the suggested approach.

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References

2005A database of German emotional speech 2005Comparing Feature Sets for Acted and Spontaneous Speech in View of Automatic Emotion Recognition 2007The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals 2006Combining Efforts for Improving Automatic Classification of Emotional User States 2006Timing levels in segment-based speech emotion recognition 2007Audiovisual Behavior Modeling by Combined Feature Spaces 2005Emotional Speech Classification Using Gaussian Mixture Models and the Sequential Floating Forward Selection Algorithm

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2012A multitask approach to continuous five-dimensional affect sensing in natural speech 2018Emotion recognition in Arabic speech 2009Data-driven clustering in emotional space for affect recognition using discriminatively trained LSTM networks

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