Emotion Classification in Children's speech using fusion of acoustic and linguistic features

Tim Polzehl, Shiva Sundaram, Hamed Ketabdar, Michael Wagner, Florian Metze

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

37 Citations (Scopus)
50 Downloads (Pure)


This paper describes a system to detect angry vs. non-angry utterances of children who are engaged in dialog with an Aibo robot dog. The system was submitted to the Interspeech2009 Emotion Challenge evaluation. The speech data consist of short utterances of the children�s speech, and the proposed system is designed to detect anger in each given chunk. Frame-based cepstral features, prosodic and acoustic features as well as glottal excitation features are extracted automatically, reduced in dimensionality and classified by means of an artificial neural network and a support vector machine. An automatic speech recognizer transcribes the words in an utterance and yields a separate classification based on the degree of emotional salience of the words. Late fusion is applied to make a final decision on anger vs. non-anger of the utterance. Preliminary results show 75.9% unweighted average recall on the training data and 67.6% on the test set.
Original languageEnglish
Title of host publication10th Annual Conference of the International Speech Communication Association (Interspeech 2009)
EditorsM Uther
Place of PublicationBrighton, UK
PublisherInternational Speech Communication Association
Number of pages4
ISBN (Print)9781615676927
Publication statusPublished - 2009
EventInterspeech-2009 - Brighton, United Kingdom
Duration: 6 Sept 20099 Sept 2009


Country/TerritoryUnited Kingdom


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