A domain agnostic approach for opinion prediction on speech
We explore a domain agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel frequency cepstral coefficients and apply long short term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state of the art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.
PEOPLES@COLING