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Found 3 papers, 3 papers with code
Multi-Task Learning for Front-End Text Processing in TTS
We propose a multi-task learning (MTL) model for jointly performing three tasks that are commonly solved in a text-to-speech (TTS) front-end: text normalization (TN), part-of-speech (POS) tagging, and homograph disambiguation (HD).
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes.
End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
Zero-shot voice conversion is becoming an increasingly popular research topic, as it promises the ability to transform speech to sound like any speaker.
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