Improving Automatic Emotion Recognition from Speech via Gender Differentiaion

Thurid Vogt · Elisabeth André

138 citations0/0 files storedLanguage Resources and Evaluation Conference (LREC)

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

Abstract

Feature extraction is still a disputed issue for the recognition of emotions from speech.Differences in features for male and female speakers are a well-known problem and it is established that gender-dependent emotion recognizers perform better than gender-independent ones.We propose a way to improve the discriminative quality of gender-dependent features: The emotion recognition system is preceded by an automatic gender detection that decides upon which of two gender-dependent emotion classifiers is used to classify an utterance.This framework was tested on two different databases, one with emotional speech produced by actors and one with spontaneous emotional speech from a Wizard-of-Oz setting.Gender detection achieved an accuracy of about 90 % and the combined gender and emotion recognition system improved the overall recognition rate of a gender-independent emotion recognition system by 2-4 %.

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2005A database of German emotional speech 2005Toward detecting emotions in spoken dialogs 2003The production and recognition of emotions in speech: features and algorithms 2003How to find trouble in communication 2005Comparing Feature Sets for Acted and Spontaneous Speech in View of Automatic Emotion Recognition

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