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Kunio Kashino - One of the best experts on this subject based on the ideXlab platform.
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Statistical Phrase/Accent Command Estimation Algorithm Utilizing Linguistic Information
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Ryotaro Sato, Kunio KashinoAbstract:The importance of extracting non-Linguistic Information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such Information, fundamental frequency (F0) contours are considered primarily important. The Fujisaki model is a physical model that describes a F0 contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of Linguistic Information has so far been limited, while it is known that accent commands are strongly related to Linguistic Information in many languages, and Linguistic Information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel F0 command parameter estimation method that incorporates Linguistic Information with the stochastic framework. Experiments using real speech data show that when Linguistic Information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
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ICASSP - Statistical Phrase/Accent Command Estimation Algorithm Utilizing Linguistic Information
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Ryotaro Sato, Kunio KashinoAbstract:The importance of extracting non-Linguistic Information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such Information, fundamental frequency $(F_{0})$ contours are considered primarily important. The Fujisaki model is a physical model that describes a $F_{0}$ contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of Linguistic Information has so far been limited, while it is known that accent commands are strongly related to Linguistic Information in many languages, and Linguistic Information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel $F_{0}$ command parameter estimation method that incorporates Linguistic Information with the stochastic framework. Experiments using real speech data show that when Linguistic Information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
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Representation models for aggregating Linguistic Information: issues and analysis
Aggregation Operators, 2020Co-Authors: Francisco Herrera, Enrique Herrera-viedma, Luis MartínezAbstract:The Linguistic Information has been used successfully in many areas. The aggregation of Linguistic Information is a crucial aspect. In the literature we can find different Linguistic computational models that present Linguistic aggregation operators as: (i) The computational model based on the Extension Principle, which operates over the fuzzy numbers that supports the semantics of the Linguistic labels. (ii) The symbolic one makes the computations directly over the order index of the Linguistic labels. And, (iii) the model based on the Linguistic 2-tuple representation, which uses the symbolic translation to make the Linguistic computations.Depending upon the Linguistic aggregation model, different results can be obtained. In this chapter we shall make a comparative analysis of the aggregation approaches according to the results obtained in a decision-making application.
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Decision Making with Unbalanced Linguistic Information
The 2-tuple Linguistic Model, 2015Co-Authors: Luis Martínez, Rosa M. Rodríguez, Francisco HerreraAbstract:In previous chapters the Linguistic Information has always been modelled by means of Linguistic terms uniformly and symmetrically distributed in a Linguistic term set, because it performs and adapts well to many problems. However, on some occasions the necessity of dealing with symmetrically distributed nonuniform terms in the scales arises because the problem needs preference scales in which one side of the scale has a greater granularity than the other. The managing of such a type of Linguistic unbalanced scales is quite challenging for Computing with Words even more if precise, Linguistic, and easily understandable results are required. This chapter describes a methodology to deal with unbalanced Linguistic Information that not only facilitates computation with this type of Information, but also provides a fuzzy representation that guarantees precise and Linguistic results by using the 2-tuple Linguistic model.
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A Linguistic 2-tuple multicriteria decision making model dealing with hesitant Linguistic Information
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015Co-Authors: Rosa M. Rodríguez, Luis Martínez, Francisco HerreraAbstract:Decision making has become a core research area in different fields such as evaluation, engineering, medicine, etc. Usually, decision making problems are defined in contexts with vague and imprecise Information. The use of Linguistic modeling has provided successful results in decision making problems. However, most of the Linguistic approaches are limited, because they restrict the elicitation of Linguistic Information to single Linguistic terms and sometimes due to the lack of Information, time or knowledge, decision makers hesitate among several Linguistic terms to elicit their assessments and the use of only one Linguistic term cannot reflect their assessments in a proper way. Therefore, more elaborated expressions than single Linguistic terms might support decision makers in such hesitant situations and improve the elicitation of hesitant Linguistic Information. The use of hesitant fuzzy Linguistic term sets (HFLTS), allows modeling this hesitation and facilitates the generation of comparative Linguistic expressions similar to the expressions used by human beings in real world decision making problems using context-free grammars. There are different decision making models that deal with HFLTS, however they do not provide Linguistic results as the computing with words scheme proposed to facilitate their comprehension. Therefore, the aim of this contribution is to present a multicriteria decision making model that not only improves the elicitation of hesitant Linguistic Information, but also obtains Linguistic results easy to understand by decision makers. To achieve this latter goal the proposed model will make use of the Linguistic 2-tuple model.
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Linguistic decision analysis steps for solving decision problems under Linguistic Information
Fuzzy Sets and Systems, 2000Co-Authors: Francisco Herrera, Enrique HerreraviedmaAbstract:A study on the steps to follow in Linguistic decision analysis is presented in a context of multi-criteria/multi-person decision making. Three steps are established for solving a multi-criteria decision making problem under Linguistic Information: (i) the choice of the Linguistic term set with its semantic in order to express the Linguistic performance values according to all the criteria, (ii) the choice of the aggregation operator of Linguistic Information in order to aggregate the Linguistic performance values, and (iii) the choice of the best alternatives, which is made up by two phases: (a) the aggregation of Linguistic Information for obtaining a collective Linguistic performance value on the alternatives, and (b) the exploitation of the collective Linguistic performance value in order to establish a rank ordering among the alternatives for choosing the best alternatives. Finally, an example is shown.
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Aggregation of Linguistic Information Based on a Symbolic Approach
Computing with Words in Information Intelligent Systems 1, 1999Co-Authors: Miguel Delgado, Francisco Herrera, Enrique Herrera-viedma, José L. Verdegay, M. A. VilaAbstract:Summary. A summary on the symbolic basic arithmetic operators and aggrega tion operators of Linguistic Information developed by the authors is presented. In particular, label addition, label difference, product of a label by a positive real num ber, and convex combination of labels are shown as the symbolic basic arithmetic operators, and two aggregation operators of Linguistic Information built using those tools are described. The first one, called the Linguistic Ordered Weighted Averag ing operator, is used to deal with Linguistic Information with equal importance, and the second one, called the Linguistic Weighted Averaging operator, is used to deal with weighted Linguistic Information.
Ryotaro Sato - One of the best experts on this subject based on the ideXlab platform.
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Statistical Phrase/Accent Command Estimation Algorithm Utilizing Linguistic Information
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Ryotaro Sato, Kunio KashinoAbstract:The importance of extracting non-Linguistic Information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such Information, fundamental frequency (F0) contours are considered primarily important. The Fujisaki model is a physical model that describes a F0 contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of Linguistic Information has so far been limited, while it is known that accent commands are strongly related to Linguistic Information in many languages, and Linguistic Information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel F0 command parameter estimation method that incorporates Linguistic Information with the stochastic framework. Experiments using real speech data show that when Linguistic Information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
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ICASSP - Statistical Phrase/Accent Command Estimation Algorithm Utilizing Linguistic Information
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Ryotaro Sato, Kunio KashinoAbstract:The importance of extracting non-Linguistic Information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such Information, fundamental frequency $(F_{0})$ contours are considered primarily important. The Fujisaki model is a physical model that describes a $F_{0}$ contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of Linguistic Information has so far been limited, while it is known that accent commands are strongly related to Linguistic Information in many languages, and Linguistic Information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel $F_{0}$ command parameter estimation method that incorporates Linguistic Information with the stochastic framework. Experiments using real speech data show that when Linguistic Information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
Kiyotaka Izumi - One of the best experts on this subject based on the ideXlab platform.
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Adaptation of robot's perception of fuzzy Linguistic Information by evaluating vocal cues for controlling a robot manipulator
Artificial Life and Robotics, 2010Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This article proposes a method for adapting a robot's perception of fuzzy Linguistic Information by evaluating vocal cues. The robot's perception of fuzzy Linguistic Information such as "very little" depends on the environmental arrangements and the user's expectations. Therefore, the robot's perception of the corresponding environment is modified by acquiring the user's perception through vocal cues. Fuzzy Linguistic Information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user's satisfactory level for the robot's movements and the user's willingness to change the robot's perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user's working space is used to illustrate the proposed system by a PA-10 robot manipulator.
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Attentive and corrective feedback for adapting robot's perception on fuzzy Linguistic Information
Proceedings of SICE Annual Conference 2010, 2010Co-Authors: Kiyotaka Izumi, A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo KiguchiAbstract:This paper proposes a method for understanding the fuzzy Linguistic Information based on the user's guidance. A quantitative assessment for a fuzzy Linguistic term such as “little” depends on the environmental conditions. Therefore the corrective feedbacks are utilized to adapt the robot's perception toward the corresponding environment. However, the attentive commands like “move carefully” are used to modify the evaluation process of the fuzzy Linguistic Information according to the user's desire. The primitive behaviors are evaluated by a behavior evaluation network (BEN) and a feedback evaluation system (FES) is utilized to evaluate the corrective feedbacks. An attention level controller is introduced to change the attention level based on the attentive commands. The system is adapted toward the user's perception on the fuzzy Linguistic Information based on the corrective feedbacks in the adaptation phase. Then the attentive commands are used to modify the evaluation process according to the local requirements. The proposed system is demonstrated by using a PA-10 robot manipulator and a situation of cooperative rearrangement of user's working space is considered. The adaptation of the system toward the environmental conditions by the corrective feedbacks and the capability to use the attentive feedbacks to modify the system during the work, improve the effectiveness of the system.
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RO-MAN - Adaptation of robot behaviors toward user perception on fuzzy Linguistic Information by fuzzy voice feedback
RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy Linguistic Information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy Linguistic Information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.
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Posture control of a robot manipulator by evaluating fuzzy Linguistic Information based on user feedback
2009 IEEE International Symposium on Industrial Electronics, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kiyotaka Izumi, Maki K. HabibAbstract:This paper proposes a method for controlling posture of a robot manipulator by fuzzy voice commands. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, the user's feedback is evaluated to understand the fuzzy Linguistic Information related to the posture movements. The primitive posture movements are evaluated by the behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user's feedback to correct the robot perception by adapting the BEN. The capability of evaluating fuzzy Linguistic Information based on the current context is enhanced. A selected set of posture movements are used to illustrate the system by using a PA-10 robot manipulator.
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Adaptation of robot behaviors toward user perception on fuzzy Linguistic Information by fuzzy voice feedback
RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy Linguistic Information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy Linguistic Information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.
A.g. Buddhika P. Jayasekara - One of the best experts on this subject based on the ideXlab platform.
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Adaptation of robot's perception of fuzzy Linguistic Information by evaluating vocal cues for controlling a robot manipulator
Artificial Life and Robotics, 2010Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This article proposes a method for adapting a robot's perception of fuzzy Linguistic Information by evaluating vocal cues. The robot's perception of fuzzy Linguistic Information such as "very little" depends on the environmental arrangements and the user's expectations. Therefore, the robot's perception of the corresponding environment is modified by acquiring the user's perception through vocal cues. Fuzzy Linguistic Information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user's satisfactory level for the robot's movements and the user's willingness to change the robot's perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user's working space is used to illustrate the proposed system by a PA-10 robot manipulator.
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Attentive and corrective feedback for adapting robot's perception on fuzzy Linguistic Information
Proceedings of SICE Annual Conference 2010, 2010Co-Authors: Kiyotaka Izumi, A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo KiguchiAbstract:This paper proposes a method for understanding the fuzzy Linguistic Information based on the user's guidance. A quantitative assessment for a fuzzy Linguistic term such as “little” depends on the environmental conditions. Therefore the corrective feedbacks are utilized to adapt the robot's perception toward the corresponding environment. However, the attentive commands like “move carefully” are used to modify the evaluation process of the fuzzy Linguistic Information according to the user's desire. The primitive behaviors are evaluated by a behavior evaluation network (BEN) and a feedback evaluation system (FES) is utilized to evaluate the corrective feedbacks. An attention level controller is introduced to change the attention level based on the attentive commands. The system is adapted toward the user's perception on the fuzzy Linguistic Information based on the corrective feedbacks in the adaptation phase. Then the attentive commands are used to modify the evaluation process according to the local requirements. The proposed system is demonstrated by using a PA-10 robot manipulator and a situation of cooperative rearrangement of user's working space is considered. The adaptation of the system toward the environmental conditions by the corrective feedbacks and the capability to use the attentive feedbacks to modify the system during the work, improve the effectiveness of the system.
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RO-MAN - Adaptation of robot behaviors toward user perception on fuzzy Linguistic Information by fuzzy voice feedback
RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy Linguistic Information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy Linguistic Information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.
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Posture control of a robot manipulator by evaluating fuzzy Linguistic Information based on user feedback
2009 IEEE International Symposium on Industrial Electronics, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kiyotaka Izumi, Maki K. HabibAbstract:This paper proposes a method for controlling posture of a robot manipulator by fuzzy voice commands. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, the user's feedback is evaluated to understand the fuzzy Linguistic Information related to the posture movements. The primitive posture movements are evaluated by the behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user's feedback to correct the robot perception by adapting the BEN. The capability of evaluating fuzzy Linguistic Information based on the current context is enhanced. A selected set of posture movements are used to illustrate the system by using a PA-10 robot manipulator.
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Adaptation of robot behaviors toward user perception on fuzzy Linguistic Information by fuzzy voice feedback
RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 2009Co-Authors: A.g. Buddhika P. Jayasekara, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka IzumiAbstract:This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy Linguistic Information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy Linguistic Information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy Linguistic Information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy Linguistic Information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.
