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

Komei Sugiura, Yoshinori Shiga, Hisashi Kawai, Teruhisa Misu, Chiori Hori

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number6907168
Pages (from-to)2237-2242
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Publication statusPublished - 2014 Sep 22
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: 2014 May 312014 Jun 7

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


Dive into the research topics of 'Non-monologue HMM-based speech synthesis for service robots: A cloud robotics approach'. Together they form a unique fingerprint.

Cite this