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Exploring Transfer Learning for Low Resource Emotional TTS
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Exploring Transfer Learning for Low Resource Emotional TTS
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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.
Forward citations
Cited by 3 Pith papers
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LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish
LuxEmo is a new 21-hour Luxembourgish expressive speech corpus with emotion labels, built from RTL broadcasts using automated detection plus human validation, and used to benchmark five TTS systems.
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LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish
LuxEmo is a new 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories, created via semi-automatic curation from RTL broadcasts and used to benchmark five TTS systems.
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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.
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