A Study of How to Implement a Listener Estimate Emotion in Speech

Abstract

To implement listener estimate emotion in speech, we propose an approach based on estimation emotion in speaker's speech. First, we use training data consist of speech data and emotion estimated by a listener. We collect human speech data and synthesize speech using speech synthesize technique. Next, we get syllabic features from training data using speech synthesize. We can divide speech into phonemes using speech synthesize. After getting phonemes, we make syllables based on phonemes. We get the fundamental frequency, power and time of each phoneme and calculate the statistics values and the inclination of the regression of them. Next, we make classifiers from these values. Finally, we estimate emotion using them. To evaluate our approach, we did the experiment. The experimental result does not say our approach is strong to do our goal. It shows some points we should modify to enhance our approach. Future works of our research are as follows. We collect training data using speech synthesize. We reconsider speech features for estimation of emotion and make classifiers using them. In addition, we divide emotion into s detail by features before making classifiers. And we evaluate the new approach. We will also modify our approaches to use in real-time.