Determining the Smallest Emotional Unit for Level of Arousal Classification

Most state-of-the-art emotion recognition methods are based on turn- and frame-level analysis independent from phonetic transcription. Currently "affective computing" community could not specify the smallest emotional standard unit which can be easily classified and determined by any "advanced" and "non-advanced" listener. It is known that, acoustic modeling on the smallest phonetic unit (phoneme) started a new era in automatic speech recognition: switch from speaker dependent isolated word recognition to speaker independent continuous speech recognition. In or current research we showed that phoneme can be used as as smallest unit for high and low arousal emotion classification task. We trained our classifications models on the VAM dataset material and evaluated them on speech samples from the DES dataset. For our experiments we employed two different emotion classification approaches: general (phonetic pattern independent) and phoneme-based (phonetic pattern dependent). Both classification approaches used MFFC features extracted on the frame level. Our experimental results impressively show that the proposed phoneme-based classification technique could increase emotion classification performance by about 9.68% absolute (15.98% relative). We showed that phoneme-level emotion models trained on "natural" emotions could provide impressive classification performance on dataset with acted affective content.