SPEECH SENTIMENT ANALYSIS VIA PRE-TRAINED FEATURES FROM END-TO-END ASR MODELS
Table 3: Ablation study on IEMOCAP dataset
['description RNN w/ attention + SpecAugment', 'description RNN w/ attention + SpecAugment', 'WA (%) 71.72', 'UA (%) 72.56']
We refer to it as the base model. The second and third rows are based on different sentiment decoders, MLP with pooling and RNN with pooling respectively. RNN with attention is better than RNN with pooling while RNN with pooling is better than MLP with pooling. Somewhat surprisingly, a simple MLP with ASR features achieves 68.68% accuracy which already improves over the state-of-the-art 66.6% in the literature. This demonstrates the effectiveness of pre-trained ASR models. The last row reports the accuracy of the base model when trained without SpecAugment, which is roughly 1% worse. In all our experiments, we find SpecAugment consistently helpful on different sentiment decoders and datasets.