Honda Motor Co., Ltd.

National Institute of Information and Communications Technology, Incorporated

Advanced Telecommunications Research Institute International

Kotaro Funakoshi

Mikio Nakano

Naoto Iwahashi

Hiroshi Okuno

Tomoyuki Sahata

Ryo Taguchi

Takashi Nose

Takayuki Nagai

Akira Mizutani

Ryu Iida

Satoru Noguchi

Noriyuki Kimura

Kazunori Komatani

Takaya Matsumoto

Shigeo Nishida

Taisuke Sumii

Takenobu Tokunaga

Masaki Katsumaru

Xiang Zuo

Hiromi Narimatsu

Tomoaki Nakamura

Yuichiro Fukubayashi

A classification apparatus includes: a calculation unit that outputs, as a classification result, results of classification by each of a plurality of classifiers with respect to learning data formed of data of at least two classes at a learning time and calculates a combination result value obtained by linear combination, using a combination coefficient, of results of classification by each of the plurality of classifiers with respect to the learning data to output the calculated combination result value as the classification result at a classification time; an extraction unit that extracts a correct solution class and an incorrect solution class for each of the classifiers from the classification result; a difference calculation unit that calculates a difference between the correct solution class and the incorrect solution class for each of the classifiers; a conversion unit that calculates a feature vector using the calculated difference for each of the classifiers; and a combination coefficient setting unit that learns, using a support vector machine, a value based on the feature vector with respect to a plurality of the learning data and thereby updates the combination coefficient, wherein input data is classified using the updated combination coefficient.

Classification apparatus, robot, and classification method

A reminder notification system includes a first terminal and a second terminal which include a reminder notification unit configured to provide a reminder of a predetermined task. The first terminal includes a task registration setting unit configured to register and set the predetermined task as a target task. The first terminal and the second terminal each include a task information share unit configured to share the target task that is registered and set in the task registration setting unit between the first terminal and the second terminal. At least one of the first terminal and the second terminal includes a notification terminal setting unit configured to set any of the first terminal and the second terminal as a notification terminal configured to provide the reminder on the basis of content of the target task when executable conditions of the target task are satisfied.

Reminder notification system and reminder notification method

A reminder notification system includes: a position acquisition unit configured to acquire a position of a user; a target position setting unit configured to set a target position; and a reminder notification unit configured to provide a reminder about a proximity area close to the target position set in the target position setting unit when the position of the user is in the proximity area, wherein the target position setting unit sets the target position associated with a predetermined category, and wherein the reminder notification unit provides the reminder when the position of the user arrives at at least one proximity area of a plurality of the target positions belonging to the predetermined category.

An information processing system is configured so that a terminal generates state information using information based on each of a first condition and a second condition in condition information, transmits condition information and state information to an information processing device at each reminder determination timing, receives notification information from the information processing device, provides a notification of a reminder if the state information satisfies the first condition or the second condition, and changes the reminder determination timing according to a state of charge of the terminal and the information processing device generates notification information if the acquired state information satisfies first or second condition information.

Information processing system, terminal, information processing method, information processing method of terminal, and program

An information processing system is configured so that a terminal generates state information using information based on each of a first condition and a second condition in condition information, transmits condition information and state information to an information processing device, receives notification information from the information processing device, provides a notification of a reminder if the state information satisfies the first condition or the second condition, and controls stopping of an application according to a state of charge of the terminal and the information processing device generates notification information if the acquired state information satisfies first or second condition information.

In an information processing system, a terminal includes: a condition information generation unit that uses at least one of a first condition and a second condition to generate condition information; a state information generation unit that generates state information; a transmission unit that transmits the condition and state information at a predetermined time interval; a reception unit that receives notice information; a reminder generation unit that generates a reminder when it is determined that the state information satisfies one of the first and second conditions when receiving the notice information; and a notification unit that notifies a user of the reminder. An information processing apparatus includes: an acquisition unit that acquires the condition and state information; a condition determination unit that generates the notice information when it is determined that the state information satisfies one of the first and second conditions; and an output unit that transmits the notice information.

Information processing system for reducing load on a server

A route guide device includes a current position acquisition unit that acquires a current position, an input unit that inputs a destination, a route search unit that searches for a route from the current position to the destination, a level storage unit that stores a of user's learning level of road information, a route overview generation unit that generates entire route information for the entire route on the basis of the route found through the search and the learning level stored in the level storage unit, and a notification unit that notifies of the entire route information generated by the route overview generation unit.

Route guide device and route guide method

A speech recognition apparatus includes a speech input unit that receives input speech, a phoneme recognition unit that recognizes phonemes of the input speech and generates a first phoneme sequence representing corrected speech, a matching unit that matches the first phoneme sequence with a second phoneme sequence representing original speech, and a phoneme correcting unit that corrects phonemes of the second phoneme sequence based on the matching result.

Speech recognition apparatus, speech recognition method, and speech recognition robot

A referring expression processor which uses a probabilistic model and in which referring expressions including descriptive, anaphoric and deictic expressions are understood and generated in the course of dialogue is provided. The referring expression processor according to the present invention includes: a referring expression processing section which performs at least one of understanding and generation of referring expressions using a probabilistic model constructed with a referring expression Bayesian network, each referring expression Bayesian network representing relationships between a reference domain (D) which is a set of possible referents, a referent (X) in the reference domain, a concept (C) concerning the referent and a word (W) which represents the concept; and a memory which stores data necessary for constructing the referring expression Bayesian network.

Language processor

The invention provides a dialogue-based learning apparatus through dialogue with users comprising: a speech input unit () for inputting speeches; a speech recognition unit () for recognizing the input speech; and a behavior and dialogue controller () for controlling behaviors and dialogues according to speech recognition results, wherein the behavior and dialogue controller () has a topic recognition expert () to memorise contents of utterances and to retrieve the topic that best matches the speech recognition results, and a mode switching expert () to control mode switching in accordance with a user utterance, wherein the mode switching expert switches modes in accordance with a user utterance, wherein the topic recognition expert registers a plurality words in the utterance as topics in first mode, performs searches from among the registered topics, and selects the maximum likelihood topic in second mode. Finally for every uttered word which is not found in a predetermined dictionary, it substitutes the said word with a word-graph comprising of either a known or a combination of known words in the said dictionary.

Non-dialogue-based and dialogue-based learning apparatus by substituting for uttered words undefined in a dictionary with word-graphs comprising of words defined in the dictionary

A language processor according to the present invention includes a probability calculating section () having a probabilistic model by which a probability that a set of partial referring expression pairs represents an object, each pair consisting of an attribute value expression and a part expression modified by the attribute value expression, the set of referring expression pairs including a pair concerning the whole object, is represented by a product of probabilities each of which is a probability that each partial referring expression pair represents the object. The probability that each partial referring expression pair represents the object is obtained using a probability that the object is chosen, a probability that represents the salience of a part of the object, and a probability representing relevance between the part of the object and an attribute value, a probability that the part expression is used for a part of the object, and a probability that the attribute value expression is used for an attribute value. It further includes at least one memory section () for storing values of probabilities used for the probabilistic model. The probability calculating section calculates the probability that the set of partial referring expression pairs represents the object using the values of probabilities stored in the at least one memory section and the probabilistic model.

A lexical acquisition apparatus includes: a phoneme recognition sectionfor preparing a phoneme sequence candidate from an inputted speech; a word matching sectionfor preparing a plurality of word sequences based on the phoneme sequence candidate; a discrimination sectionfor selecting, from among a plurality of word sequences, a word sequence having a high likelihood in a recognition result; an acquisition sectionfor acquiring a new word based on the word sequence selected by the discrimination section; a teaching word listA used to teach a name; and a probability modelB of the teaching word and an unknown word, wherein the discrimination sectioncalculates, for each word sequence, a first evaluation value showing how much words in the word sequence correspond to teaching words in the listA and a second evaluation value showing a probability at which the words in the word sequence are adjacent to one another and selects a word sequence for which a sum of the first evaluation value and the second evaluation value is maximum, and wherein the acquisition sectionacquires, as a new word, a word in the word sequence selected by the discrimination section that is not involved in the calculation of the first evaluation value.

Lexical acquisition apparatus, multi dialogue behavior system, and lexical acquisition program

A speech understanding apparatus includes a speech recognition unit which performs speech recognition of an utterance using multiple language models, and outputs multiple speech recognition results obtained by the speech recognition, a language understanding unit which uses multiple language understanding models to perform language understanding for each of the multiple speech recognition results output from the speech recognition unit, and outputs multiple speech understanding results obtained from the language understanding, and an integrating unit which calculates, based on values representing features of the speech understanding results, utterance batch confidences that numerically express accuracy of the speech understanding results for each of the multiple speech understanding results output from the language understanding unit, and selects one of the speech understanding results with a highest utterance batch confidence among the calculated utterance batch confidences.

Speech understanding apparatus using multiple language models and multiple language understanding models

A command recognition device includes: an utterance understanding unit that determines or selects word sequence information from speech information; speech confidence degree calculating unit that calculates degree of speech confidence based on the speech information and the word sequence information; a phrase confidence degree calculating unit that calculates a degree of phrase confidence based on image information and phrase information included in the word sequence information; and a motion control instructing unit that determines whether a command of the word sequence information should be executed based on the degree of speech confidence and the degree of phrase confidence.

Command recognition device, command recognition method, and command recognition robot

A question and answer database expansion apparatus includes: a question and answer database in which questions and answers corresponding to the questions are registered in association with each other, a first speech recognition unit which carries out speech recognition for an input sound signal by using a language model based on the question and answer database, and outputs a first speech recognition result as the recognition result, a second speech recognition unit which carries out speech recognition for the input sound signal by using a language model based on a large vocabulary database, and outputs a second speech recognition result as the recognition result, and a question detection unit which detects an unregistered utterance, which is not registered in the question and answer database, from the input sound based on the first speech recognition result and the second speech recognition result, and outputs the detected unregistered utterance.

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Wako, Japan

Kotaro Funakoshi