The Performance of the Speaking Rate Parameter in Emotion Recognition from Speech

The speaking rate is a quite obvious prosodic characteristic of speech and humans can easily estimate how fast an interlocutor is talking. Further, different emotional dispositions of a person are strongly expressed in his/her speaking rate. In this paper we investigate the performance gain originating from the use of the speaking rate parameter in emotion recognition from speech. The speaking rates are determined by applying a broad phonetic class recognizer. The classifier is trained on cepstral features extracted on the emotionally neutral RM1 speech corpus and provides low average recognition errors of one phoneme/second. We present the results of an empirical approach on the emotionally expressive Emo-DB corpus applying a neural network classifier and prove the significant influence of the speaking rate in emotion classification. The performances of Multi-Layer Perceptrons trained on cepstral turn-level features are analyzed with respect to the presence and absence of the speaking rate feature. An increase of accuracy up to 3.7% in certain emotion categories is reported.