Currently, smartphones and tablets are firmly implanted within our daily lives. These devices have an entire ecosystem devoted to them, with applications and tools designed for their specifications: they use touch-enabled interfaces, have a limited amount of memory and CPU time available for apps (16/32MB limit on Android and iOS devices). A well-established research domain is the development of natural human-computer-interfaces (HCI) via voice and gestures. However, these interfaces are bound by the hardware resources available to them, and by the fact that they use network/Internet access to send/receive data, relying on dedicated servers for the decision making process. This paper focuses on the development of small robust deep-learning models that are designed to provide high quality text-to-speech (TTS) functionality (one of the three main components of HCI) on smart devices, without requiring network access. We obtain very good results in TTS text sub-tasks using models significantly smaller than those used in state-of-the-art approaches.
['Tiberiu Boros', 'Stefan Daniel Dumitrescu']
Robust deep-learning models for text-to-speech synthesis support on embedded devices