A methodology is proposed for emotional text-to-speech using emotional data collection, transfer-learning-based annotation of expressiveness features, and fine-tuning of a neutral TTS model.
Exploring Transfer Learning for Low Resource Emotional TTS
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abstract
During the last few years, spoken language technologies have known a big improvement thanks to Deep Learning. However Deep Learning-based algorithms require amounts of data that are often difficult and costly to gather. Particularly, modeling the variability in speech of different speakers, different styles or different emotions with few data remains challenging. In this paper, we investigate how to leverage fine-tuning on a pre-trained Deep Learning-based TTS model to synthesize speech with a small dataset of another speaker. Then we investigate the possibility to adapt this model to have emotional TTS by fine-tuning the neutral TTS model with a small emotional dataset.
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eess.AS 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach
A methodology is proposed for emotional text-to-speech using emotional data collection, transfer-learning-based annotation of expressiveness features, and fine-tuning of a neutral TTS model.