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Intelligent Speech Communication Using Double Humanoid Robots


Authors



Abstract

Speech recognition is one of the most convenient forms of human beings engaging in the exchanging of information. In this research, we want to make robots understand human language and communicate with each other through the human language, and to realize man-machine interactive and humanoid-robot interactive. Therefore, this research mainly studies NAO robots' speech recognition and humanoid communication between double-humanoid robots. This paper introduces the future direction and application prospect of speech recognition as well as its basic method and knowledge of speech recognition fields. This research also proposes the application of the most advanced method--establishment of the Hidden Markov Model (HMM) for the continuous word recognition in the speech recognition of NAO robots. In addition, this paper focuses on the establishment of a modelling algorithm and the extraction method of speech characteristics and the existing problems. Meanwhile, this research demonstrates the experiments of the NAO robot structured function and the voice interactive of double-humanoid robots. Through the use of an NAO-robot-controlled platform, Choregraphe software, and combined calling of Baidu speech recognition, the communication between double-humanoid robots has been achieved. Besides, a series of programming designs have realized the interactive functions such as NAO robots' daily dialogue and communication, arithmetic function (addition, subtraction, multiplication, and division), singing, making movement, and rhetorical-pattern dialogues. Finally, it shows the significance and contribution of research of interaction between double-humanoid robots.


Keywords


Pages

Total Pages: 11

DOI
10.31209/2020.100000164


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Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

TWO YEAR CITATIONS PER DOCUMENT:
2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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