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.
W. Han et al. proposed to treat an utterance's label sequence that was used to put into the RNN based connectionist temporal classification modal to judge the emotional segments' labels, and then got the better results compared to other algorithm to demonstrate the effectiveness REF .
Towards Temporal Modelling of Categorical Speech Emotion Recognition
INTERSPEECH
Computer Science