Emotion and Computing – Current Research and Future Impact

Speech signal in addition to the linguistic information contains additional information about the speaker: age, gender, social status, accent (foreign accent, dialects, etc.), emotional state, health etc. Some of these informational channels induce changes of the speech acoustic characteristics. This article presents evaluation of the ASR acoustic models (first trained on neutral, read speech) on acted and spontaneous emotional speech. In our research we used adaptation approaches to compensate the mismatch of acoustic characteristics between neutral speech samples and affective speech material. During experiments we observed that the affective-speech-adapted ASR acoustic models provide better emotional-speech-recognition performance. The improvements of affective speech recognition performance were 6.24% absolute (7.1% relative) for speaker-independent evaluations on the EMO-DB database and 7.08% absolute (25.43% relative) for cross-corpora evaluation on the VAM database.