Towards Temporal Modelling of Categorical Speech Emotion Recognition

Abstract

To model the categorical speech emotion recognition task in a temporal manner, the first challenge arising is how to transfer the categorical label for each utterance into a label sequence.To settle this, we make a hypothesis that an utterance is consisting of emotional and non-emotional segments, and these non-emotional segments correspond to silent regions, short pauses, transitions between phonemes, unvoiced phonemes, etc.With this hypothesis, we propose to treat an utterance's label sequence as a chain of two states: the emotional state denoting the emotional frame and Null denoting the non-emotional frame.Then, we exploit a recurrent neural network based connectionist temporal classification model to automatically label and align an utterance's emotional segments with emotional labels, while non-emotional segments with Nulls.Experimental results on the IEMOCAP corpus validate our hypothesis and also demonstrate the effectiveness of our proposed method compared to the state-of-the-art algorithms.