Anger Recognition in Spoken Dialog Using Linguistic and Para-Linguistic Information

This paper proposes a method to recognize anger-dialog based on linguistic and para-linguistic information in speech. Anger is classified into two types; HotAnger (agitated) and ColdAnger (calm). Conventional prosody-features based on para-linguistic can reliably recognize the former but not the latter. To recognize anger more robustly, we apply other para-linguistic cues named dialog-features which are seen in conversational interactive situations between two speakers such as turn-taking and backchannel feedback. We also utilize linguistic-features which represent conversational emotional salience. They are acquired by Pearson’s chi-square test by comparing the automaticallytranscribed texts between angry and neutral dialogs. Experiments show that the proposed feature combination improves the F-measure of ColdAnger and HotAnger by 26.9 points and 16.1 points against a baseline that uses only prosody.