Google Research

Speech Sentiment Analysis via End-To-End ASR Features

(to appear)


In this paper, we propose to use end-to-end ASR features to solve the speech sentiment as a down-stream task. We show that end-to-end ASR features integrate the benefits from both acoustic models and language models. From the sequence of ASR features, we develop effective methods to recognize sentiment and get promising results. Our approach improves the-state-of-the-art accuracy on IEMOCAP from 66.6% to 71.7%, and achieves an accuracy of 70.10% on SWBD-sentiment with more than 49,500 utterances.

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