Emotion recognition from speech: Putting ASR in the loop

Björn Wolfgang Schuller · Anton M. Batliner · Stefan Steidl · Dino Seppi

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

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Emotion and Mood Recognition Speech Recognition and Synthesis Sentiment Analysis and Opinion Mining

Abstract

This paper investigates the automatic recognition of emotion from spoken words by vector space modeling vs. string kernels which have not been investigated in this respect, yet. Apart from the spoken content directly, we integrate part-of-speech and higher semantic tagging in our analyses. As opposed to most works in the field, we evaluate the performance with an ASR engine in the loop. Extensive experiments are run on the FAU Aibo Emotion Corpus of 4 k spontaneous emotional child-robot interactions and show surprisingly low performance degradation with real ASR over transcription-based emotion recognition. In the result, bag of words dominate over all other modeling forms based on the spoken content.

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References

2002Prosody-based automatic detection of annoyance and frustration in human-computer dialog 2007The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals 2002Combining acoustic and language information for emotion recognition 2005Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles 2000Emotion-sensitive human-computer interfaces 2007Towards More Reality in the Recognition of Emotional Speech 2006Combining Efforts for Improving Automatic Classification of Emotional User States 2014Text-to-Emotion Engine for Real Time Internet Communication

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2013Robust emotion recognition in noisy speech via sparse representation

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