Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System

Proceedings of the 28th International Conference on Computational Linguistics, pages 5013–5025 Barcelona, Spain (Online), December 8-13, 2020 5013 Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System Hiroaki Takatsu1 Ryota Ando2 Yoichi Matsuyama1 Tetsunori Kobayashi1 1Waseda University, Tokyo, Japan 2Naigai Pressclipping Bureau,Ltd., Tokyo, Japan takatsu@pcl.cs.waseda.ac.jp, ando@naigaipc.co.jp, matsuyama@pcl.cs.waseda.ac.jp, koba@waseda.jp Abstract As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased. In this paper, to control the emotional parameters of the speech synthesis according to certain dialogue contents, we construct a news dataset with emotion labels (“positive,” “negative,” or “neutral”) annotated for each sentence. We then propose a method to identify emotion labels using a model combining BERT and BiLSTM-CRF, and evaluate its ef- fectiveness using the constructed dataset. The results showed that the classification model perfor- mance can be efficiently improved by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework. 1 Introduction As smart speakers and conversational robots become ubiquitous, the demand for expressive speech syn- thesis has increased. Speech synthesis is a technology that converts input text into a speech signal that represents its contents. In recent years, research has not only improved the quality so that a synthesized voice is similar to a real one, but also has diversified expressions (Govind and Prasanna, 2013; Kaur and Singh, 2015; Skerry-Ryan et al., 2018). Emotional speech synthesis is a technique for diversifying the expression of speech synthesis (Qin et al., 2006; Schr¨oder, 2001; Yang et al., 2018). It specifies both emotional parameters and the text input so that the speech reflects the designated emotion (Charfuelan and Steiner, 2013; Inoue et al., 2017; Iwata and Kobayashi, 2011; Nose and Kobayashi, 2013). In conversational robots and non-task-oriented dialogue systems, the emotions of speech synthesis are controlled not only by the contents of the text spoken by the system but also by the personality of the robot or environmental information (Bennewitz et al., 2007; Chiba et al., 2018; Robbel et al., 2009). On the other hand, in audiobooks and news reading systems, the emotions when the system speaks are often determined by the content of the text. In such applications, it is desirable that the emotional parameters used in the speech synthesis system be automatically estimated from the input text (Bellegarda, 2011; Jauk et al., 2018; Shaikh et al., 2009; Sudhakar and Bensraj, 2014; Trilla and Al´ıas, 2013; Vanmassenhove et al., 2016). When news is transmitted by a synthesized voice, it is beneficial for listeners that news with positive content is transmitted with voices that are synthesized with positive emotion, whereas news with negative content is transmitted with voices that are synthesized with negative emotion (Pitrelli et al., 2006). In our spoken dialogue system that delivers news (Takatsu et al., 2018), it is important to speak clearly with emotion according to the content of the news to improve the users’ understanding. Table 1 shows an example of a news conversation. S2 is “positive” because it reports that the subject, Nishikori, won …

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