Fusion of Acoustic and Linguistic Speech Features for Emotion Detection

This paper describes a system that deploys acoustic and linguistic information from speech in order to decide whether the utterance contains negative or nonnegative meaning. An earlier version of this system was submitted to the Interspeech-2009 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. Various frame-based cepstral, prosodic and acoustic features are extracted automatically and classified by means of a support vector machine. An automatic speech recognizer transcribes the utterances and yields a separate classification, based on the degree of emotional salience of the words. The emotionally salient words are computed on word hypotheses, so that un-transcribed training data is sufficient. Late fusion is applied to make a final decision on anger vs. non-anger of the utterance.