Non-monologue HMM-based speech synthesis for service robots: A cloud robotics approach

Abstract

Robot utterances generally sound monotonous, unnatural, and unfriendly because their Text-to-Speech (TTS) systems are not optimized for communication but for text-reading. Here we present a non-monologue speech synthesis for robots. We collected a speech corpus in a non-monologue style in which two professional voice talents read scripted dialogues. Hidden Markov models (HMMs) were then trained with the corpus and used for speech synthesis. We conducted experiments in which the proposed method was evaluated by 24 subjects in three scenarios: text-reading, dialogue, and domestic service robot (DSR) scenarios. In the DSR scenario, we used a physical robot and compared our proposed method with a baseline method using the standard Mean Opinion Score (MOS) criterion. Our experimental results showed that our proposed method's performance was (1) at the same level as the baseline method in the text-reading scenario and (2) exceeded it in the DSR scenario. We deployed our proposed system as a cloud-based speech synthesis service so that it can be used without any cost.