Improving Automatic Emotion Recognition from Speech via Gender Differentiaion

By Thurid Vogt, Elisabeth André. 2006

178 citations recorded by the source corpus.

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 %.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Signal Processing

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Citations in the corpus

Comparison of Gender- and Speaker-adaptive Emotion Recognition (2014, background): “The rest of the paper is organized as follows: Signiﬁcant related work on emotion recognition including the gender-adaptive approach by Vogt and Andr´e is presented in the 2.”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “Several studies indicate that considering gender information in an automatic emotion recognition system leads to higher recognition accuracies (Ververidis and Kotropoulos, 2004; Vogt and Andre, 2006); however, it is not settled yet whether this is simply due to the different pitch registers of male ”
Gender classification in two Emotional Speech databases (2008, methodology): “That is, our approach, when compared to [13] yields a 9.”
Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, background): “Generally, automatic gender recognition results in a great accuracy without much effort because the results of such type of recognition is binary (either male or female) [32], [33].”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, methodology): “To this end, traditional segmental features like MFCCs or LPCCs are transformed to suprasegmental parameterizations through long-time statistic processing, in order to be concatenated to the prosodic feature vector (Kwon et al. 2003; Vogt and André 2006; Schuller et al. 2005a).”