title: STTATTS: Unified Speech-To-Text And Text-To-Speech Model
abstract: Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks.We propose a parameterefficient approach to learning ASR and TTS jointly via a multi-task learning objective and shared parameters.Our evaluation demonstrates that the performance of our multi-task model is comparable to that of individually trained models while significantly saving computation