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Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method

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@article{IJASEIT7606,
   author = {Dinda Ayu Permatasari and Hanif Fakhrurroja and Carmadi Machbub},
   title = {Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {1881--1888},
   keywords = {dialogue manager; TF-IDF; cosine similarity; finite state machine; human-robot interaction; Google cloud speech.},
   abstract = {

Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. 

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7606},    doi = {10.18517/ijaseit.10.5.7606} }

EndNote

%A Permatasari, Dinda Ayu
%A Fakhrurroja, Hanif
%A Machbub, Carmadi
%D 2020
%T Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method
%B 2020
%9 dialogue manager; TF-IDF; cosine similarity; finite state machine; human-robot interaction; Google cloud speech.
%! Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method
%K dialogue manager; TF-IDF; cosine similarity; finite state machine; human-robot interaction; Google cloud speech.
%X 

Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. 

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7606 %R doi:10.18517/ijaseit.10.5.7606 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 5 %@ 2088-5334

IEEE

Dinda Ayu Permatasari,Hanif Fakhrurroja and Carmadi Machbub,"Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 1881-1888, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.7606.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Permatasari, Dinda Ayu
AU  - Fakhrurroja, Hanif
AU  - Machbub, Carmadi
PY  - 2020
TI  - Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 1881
EP  - 1888
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - dialogue manager; TF-IDF; cosine similarity; finite state machine; human-robot interaction; Google cloud speech.
N2  - 

Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. 

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7606 DO - 10.18517/ijaseit.10.5.7606

RefWorks

RT Journal Article
ID 7606
A1 Permatasari, Dinda Ayu
A1 Fakhrurroja, Hanif
A1 Machbub, Carmadi
T1 Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 1881
OP 1888
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 dialogue manager; TF-IDF; cosine similarity; finite state machine; human-robot interaction; Google cloud speech.
AB 

Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. 

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7606 DO - 10.18517/ijaseit.10.5.7606