Honda Motor Co., Ltd.

Kobe University

National Institute of Information and Communications Technology, Incorporated

Advanced Telecommunications Research Institute International

Mikio Nakano

Kotaro Funakoshi

Naoto Iwahashi

Hiroshi Okuno

Kazunori Komatani

Tomoyuki Sahata

Yasuo Ariki

Yuko Ozasa

Ryo Taguchi

Takashi Nose

Takayuki Nagai

Hiroshi Tsujino

Akira Mizutani

Ryu Iida

Noriyuki Kimura

Yohane Takeuchi

Tsugumi Otsuka

Hitoshi Nishimura

Taisuke Sumii

Takahiro Hori

Ryohei Nakatani

Takenobu Tokunaga

Masaki Katsumaru

Xiang Zuo

Hiromi Narimatsu

Tomoaki Nakamura

Yuichiro Fukubayashi

An object authentication device includes a speech recognition unit configured to obtain candidates for a speech recognition result for an input speech and a likelihood of the speech as a speech likelihood and an image model generation unit configured to obtain image models of a predetermined number of candidates for the speech recognition result in descending order of speech likelihoods, wherein the image model generation unit initially performs retrieval from an image model database storing the image models when the image models for the candidates for the speech recognition result are generated and generates an image model from information acquired from a network if the image model is not stored in the image model database.

Object authentication device and object authentication method

An object authentication device includes a speech recognition unit configured to obtain candidates for a speech recognition result for an input speech and a likelihood of the speech as a speech likelihood, an image model generation unit configured to obtain image models of a predetermined number of candidates for the speech recognition result in descending order of speech likelihoods, an image likelihood calculation unit configured to obtain an image likelihood based on an image model of an input image, and an object authentication unit configured to perform object authentication using the image likelihood, wherein vocabularies predicted through speech recognition are categorized and the image model is formed in association with a category.

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.

Reminder notification system and reminder notification method

An object recognition system is applicable to practical use, and utilizes image information besides speech information to improve recognition accuracy. The object recognition system comprises a speech recognition unit to determine candidates for a result of speech recognition on input speech and their likelihoods, and an image model generation unit to generate image models of a predetermined number of the candidates having the highest likelihoods. The system further comprises an image likelihood calculation unit to calculate image likelihoods of input images based on the image models, and an object recognition unit to perform object recognition using the image likelihoods. At the time of generating the image model of the candidate, the image model generation unit first searches an image model database, and, when the image model of the candidate is not found in the database, the image model generation unit generates said image model from image information on the web.

Object recognition system and an object recognition method

A speech dialogue system generates a response sentence in a way to improve the efficiency of the dialogue with the user, based on a result of estimation on an attribute of a proper name in an utterance of a user. The system includes a database attribute estimation unit to estimate the attribute of the input proper name by utilizing a database, and a web attribute estimation unit to estimate an attribute of an input proper name by utilizing information on the web. A reliability integration unit calculates integrated reliability of estimation for each of possible attributes obtained from the estimation by the units, by integrating first reliability of the estimation. A response generation unit generates a response sentence to an input utterance based on the integrated reliabilities of the possible attributes.

Speech dialogue system and speech dialogue method

A first domain satisfying a first condition concerning a current utterance understanding result and a second domain satisfying a second condition concerning a selection history are specified. For each of the first and second domains, indices representing reliability in consideration of the utterance understanding history, selection history, and utterance generation history are evaluated. Based on the evaluation results, one of the first, second, and third domains is selected as a current domain according to a selection rule.

Conversation system and conversation software

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

An apparatus is provided for classifying targets into a known-object group and an unknown-object group. The apparatus includes a speech/image data storage unit configured to store a spoken sound of a name of an object and an image of the object; a unit configured to calculate a speech confidence level of a speech for the name of the object with reference to a spoken sound of a name of a known object; a unit configured to calculate an image confidence level of an image of an object with respect to an image of a known object; and a unit configured to compare an evaluation value, which is obtained by combining the speech confidence level and image confidence level, with a threshold value, and classify a target object into an object group determined according to whether the spoken sound of the name and the image are known or unknown.

Object classification/recognition apparatus and method

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 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.

Question and answer database expansion based on speech recognition using a specialized and a general language model

An image capturing device is equipped with an external parameter estimating unit for estimating external parameters using a distance image obtained by a TOF camera and a luminance image obtained by a CCD camera, a corresponding pixel determining unit for determining a correspondence relationship between pixel positions in the distance image and pixel positions in the luminance image, using previously stored internal parameters of the TOF camera and the CCD camera or the CCD camera, and the external parameters, and an occlusion searching unit for searching for an occlusion region in the distance image, using the correspondence relationship between pixel positions in the distance image and pixel positions in the luminance image.

Growing community of inventors

Wako, Japan

Mikio Nakano