Abstract:
An audio output unit and method thereof for generating a natural and understandable composite tone as a whole. A fundamental frequency can greatly be reduced at a meaningful boundary of voice contents and a voice strictly reflecting a syntax structure can be outputted by changing the reduction characteristic of the phrase component of the fundamental frequency and thereby controlling the response characteristic of a secondary linear system to the phrase component to calculate the phrase component, so that it is possible to easily generate a natural and understandable composite tone as a whole.
Abstract:
The learning apparatus acquires the name of an object by interacting with a person, and stores the acquired data by correlating with different feature data of the object, in a memory (65). A new object is recognized based on the stored data, and related information of the recognized object, are stored. Independent claims are also included for the following: (1) learning method; and (2) robot.
Abstract:
An voice synthesizing unit performs voice synthesizing processing, based on the state of emotion of a robot at an emotion/instinct model unit. For example, in the event that the emotion state of the robot represents "not angry", synthesized sound of "What is it?" is generated at the voice synthesizing unit. On the other hand, in the event that the emotion state of the robot represents "angry", synthesized sound of "Yeah, what?" is generated at the voice synthesizing unit, to express the anger. Thus, a robot with a high entertainment nature is provided.
Abstract:
An voice synthesizing unit performs voice synthesizing processing, based on the state of emotion of a robot at an emotion/instinct model unit. For example, in the event that the emotion state of the robot represents "not angry", synthesized sound of "What is it?" is generated at the voice synthesizing unit. On the other hand, in the event that the emotion state of the robot represents "angry", synthesized sound of "Yeah, what?" is generated at the voice synthesizing unit, to express the anger. Thus, a robot with a high entertainment nature is provided.
Abstract:
The learning apparatus acquires the name of an object by interacting with a person, and stores the acquired data by correlating with different feature data of the object, in a memory (65). A new object is recognized based on the stored data, and related information of the recognized object, are stored. Independent claims are also included for the following: (1) learning method; and (2) robot.
Abstract:
Voice processing for recognizing a predetermined voice such as a place name, etc. is performed by a voice processing section 14 from an audio signal inputted from a microphone 11 on the basis of the operation of an operating means 18. When a map display, etc. based on the recognized place name, etc. are performed, an incorrect reading way and a place name tending to be mistaken can be also recognized. Accordingly, a high grade operation of a navigation apparatus can be simply performed without obstructing driving of a car, etc.
Abstract:
Voice processing for recognizing a predetermined voice such as a place name, etc. is performed by a voice processing section 14 from an audio signal inputted from a microphone 11 on the basis of the operation of an operating means 18. When a map display, etc. based on the recognized place name, etc. are performed, an incorrect reading way and a place name tending to be mistaken can be also recognized. Accordingly, a high grade operation of a navigation apparatus can be simply performed without obstructing driving of a car, etc.
Abstract:
A conventional robot apparatus or the like cannot learn a name in a natural way. A learning apparatus can acquire a name of an object through interaction with a human and stores it in correlation with a plurality of different feature data detected for the object. According to these data stored and information on correlation, the apparatus recognizes a new object, acquires a name and feature data of the new object (including a human), and stores information on correlation between them, thereby successively learning names of objects. A learning method and a robot apparatus are also disclosed.
Abstract:
A preliminary word-selecting section selects one or more words following words which have been obtained in a word string serving as a candidate for a result of voice recognition; and a matching section calculates acoustic or linguistic scores for the selected words, and forms a word string serving as a candidate for a result of voice recognition according to the scores. A control section generates word-connection relationships between words in the word string serving as a candidate for a result of voice recognition, sends them to a word-connection-information storage section, and stores them in it. A re-evaluation section corrects the word-connection relationships stored in the word-connection-information storage section 16, and the control section determines a word string serving as the result of voice recognition according to the corrected word-connection relationships.