Exploring Transfer Learning for Low Resource Emotional TTS
Published in
Intelligent Systems with Applications(2019)External Links:
TL
TL;DR
This paper investigates how to leverage fine-tuning on a pre-trained Deep Learning-based TTS model to synthesize speech with a small dataset of another speaker, and adapts this model to have emotional TTS by fine- Tuning the neutral T TS model with asmall emotional dataset.
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.
References23 items
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The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
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Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
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ASR-based Features for Emotion Recognition: A Transfer Learning Approach
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Efficient Neural Audio Synthesis
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