Combining frame and turn-level information for robust recognition of emotions within speech

Bogdan Vlasenko · Björn Wolfgang Schuller · Andreas Wendemuth · Gerhard Rigoll

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

Abstract

Current approaches to the recognition of emotion within speech usually use statistic feature information obtained by application of functionals on turn- or chunk levels. Yet, it is well known that thereby important information on temporal sub-layers as the frame-level is lost. We therefore investigate the benefits of integration of such information within turn-level feature space. For frame-level analysis we use GMM for classification and 39 MFCC and energy features with CMS. In a subsequent step output scores are fed forward into a 1.4k large-feature-space turn-level SVM emotion recognition engine. Thereby we use a variety of Low-Level-Descriptors and functionals to cover prosodic, speech quality, and articulatory aspects. Extensive testruns are carried out on the public databases EMO-DB and SUSAS. Speaker-independent analysis is faced by speaker normalization. Overall results highly emphasize the benefits of feature integration on diverse time scales.

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References

2005A database of German emotional speech 2003Hidden Markov model-based speech emotion recognition 1997Getting started with SUSAS: a speech under simulated and actual stress database 2007Towards More Reality in the Recognition of Emotional Speech 2006Timing levels in segment-based speech emotion recognition

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2010The Automatic Recognition of Emotions in Speech 2010Segmenting into Adequate Units for Automatic Recognition of Emotion-Related Episodes: A Speech-Based Approach 2022Speaker Normalization for Self-Supervised Speech Emotion Recognition 2019Improving multilingual speech emotion recognition by combining acoustic features in a three-layer model 2011Application of speaker- and language identification state-of-the-art techniques for emotion recognition 2011Automatic stress detection in emergency (telephone) calls 2009Automatic recognition of speech emotion using long-term spectro-temporal features

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