Multi-Task Learning for Front-End Text Processing in TTS

• URL: http://arxiv.org/abs/2401.06321v1
• Date: Fri, 12 Jan 2024 02:13:21 GMT
• Title: Multi-Task Learning for Front-End Text Processing in TTS
• Authors: Wonjune Kang, Yun Wang, Shun Zhang, Arthur Hinsvark, Qing He
• Abstract summary: We propose a multi-task learning (MTL) model for jointly performing three tasks that are commonly solved in a text-to-speech front-end. Our framework utilizes a tree-like structure with a trunk that learns shared representations, followed by separate task-specific heads.
• Score: 15.62497569424995
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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). Our framework utilizes a tree-like structure with a trunk that learns shared representations, followed by separate task-specific heads. We further incorporate a pre-trained language model to utilize its built-in lexical and contextual knowledge, and study how to best use its embeddings so as to most effectively benefit our multi-task model. Through task-wise ablations, we show that our full model trained on all three tasks achieves the strongest overall performance compared to models trained on individual or sub-combinations of tasks, confirming the advantages of our MTL framework. Finally, we introduce a new HD dataset containing a balanced number of sentences in diverse contexts for a variety of homographs and their pronunciations. We demonstrate that incorporating this dataset into training significantly improves HD performance over only using a commonly used, but imbalanced, pre-existing dataset.

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