Robot utterances generally sound monotonous, unnatural and unfriendly because their Text-to-Speech systems are not optimized for communication but for text reading. Here, we present a non-monologue speech synthesis for robots. The key novelty lies in speech synthesis based on Hidden Markov models (HMMs) using a non-monologue corpus: we collected a speech corpus in a non-monologue style in which two professional voice talents read scripted dialogues, and 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 criterion. Our experimental results showed that our proposed methods 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.
ASJC Scopus subject areas
- Control and Systems Engineering
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications