SPEECH SENTIMENT ANALYSIS VIA PRE-TRAINED FEATURES FROM END-TO-END ASR MODELS
Table 2: Speech sentiment analysis performances of different methods.
['Input features', 'IEMOCAP dataset Architecture', 'IEMOCAP dataset WA (%)', 'IEMOCAP dataset UA (%)', 'SWBD-sentiment dataset Architecture', 'SWBD-sentiment dataset WA (%)', 'SWBD-sentiment dataset UA (%)']
We provide a reference of the human performance, which is essentially the average agreement percentage among annotators. Since sentiment evaluation is subjective, human performance is an upper bound on the accuracies. Comparing human performance on SWBD-sentiment with IEMOCAP, we confirm that SWBD-sentiment is more challenging.