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). 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.Comment: ICASSP 202

Game Development Process of The Hauntlet

In 2015, the Game Development Club at Embry-Riddle Aeronautical University had the unique opportunity of creating a rewarding horror experience for virtual reality based on iterative user testing and development. Outsiders to the UX/UI design domain, the Game Development Club was challenged to develop this engaging virtual environment on a bootstrap budget, and with experience based solely on previous gaming familiarity. The Game Development Club conducted user testing and iteratively designed all aspects of the game based on rounds of user testing. The GDC enabled future users to become actively involved in the game development process; a lesson for other game development teams and those in industry