Electronics and Telecommunications Research Institute

Ji Hyun Wang

Myung Gil Jang

Jeon Gue Park

Hoon Chung

Byung Ok Kang

Sung Joo Lee

Jeom Ja Kang

Ho-Young Jung

Hyung-Bae Jeon

Chung Hee Lee

Jong Jin Kim

Yunkeun Lee

Ki-young Park

Euisok Chung

Jeong Mu Heo

Hyun Ki Kim

Mi Ran Choi

Yong Jin Bae

Joon Ho Lim

Hyung Jik Lee

Soo Jong Lim

Sang Kyu Park

Hyo Jung Oh

Eui Sok Chung

Kyung Man Bae

Chang Ki Lee

Yun Keun Lee

Young Jik Lee

Min Ho Kim

Hyeon Jin Kim

Yi Gyu Hwang

Ji Hee Ryu

A chatbot system and method conversing with other chatbots in place of a user. The chatbot system and method understand the purpose of conversation of a user, select chatbots to which the purpose of the user is to be transferred, converse with the selected chatbots in place of the user, and present the user with a result of conversation undertaken. The chatbot system converses with provider chatbots provided by property and service providers. The chatbot system includes a consumer chatbot that understands a purpose of conversation of a user, converses with the provider chatbots in place of the user, and provides a result of the conversation to the user.

Chatbot system and service method thereof

The language analysis method includes collecting an online document, performing coined word filtering on the collected document based on a predetermined coined word extraction pattern and a dictionary to extract a coined word candidate which corresponds to the coined word extraction pattern and is not registered in a coined stop word dictionary, performing morpheme analysis on the extracted coined word candidate to verify a coined word candidate which does not correspond to a predetermined part of speech and is not registered in a pre-stored morpheme dictionary, refining the coined word extraction pattern, based on a result of the verification and updating the verified coined word candidate in the coined stop word dictionary, and updating the verified coined word candidate in the morpheme dictionary, based on the result of the verification.

Online contextual advertisement intellectualization apparatus and method based on language analysis for automatically recognizing coined word

Provided are an interactive question answering apparatus and method, which improve an unnatural question sentence and prevent the impossibility of a question in a state which does not know additional information, thereby providing a multimodal-based question answering method enabling a person to provide a question like conversing. Also, a question answering service is exposed to potential product purchasers of content such as an image, video, audio, etc., thereby providing an environment available to advertisement market.

Interactive question-answering apparatus and method thereof

A speech recognition system includes: a speed level classifier for measuring a moving speed of a moving object by using a noise signal at an initial time of speech recognition to determine a speed level of the moving object; a first speech enhancement unit for enhancing sound quality of an input speech signal of the speech recognition by using a Wiener filter, if the speed level of the moving object is equal to or lower than a specific level; and a second speech enhancement unit enhancing the sound quality of the input speech signal by using a Gaussian mixture model, if the speed level of the moving object is higher than the specific level. The system further includes an end point detection unit for detecting start and end points, an elimination unit for eliminating sudden noise components based on a sudden noise Gaussian mixture model.

Noise reduction for speech recognition in a moving vehicle

An utterance verification method for an isolated word N-best speech recognition result includes: calculating log likelihoods of a context-dependent phoneme and an anti-phoneme model based on an N-best speech recognition result for an input utterance; measuring a confidence score of an N-best speech-recognized word using the log likelihoods; calculating distance between phonemes for the N-best speech-recognized word; comparing the confidence score with a threshold and the distance with a predetermined mean of distances; and accepting the N-best speech-recognized word when the compared results for the confidence score and the distance correspond to acceptance.

Utterance verification method and apparatus for isolated word N-best recognition result

A method for separating a sound source from a mixed signal, includes Transforming a mixed signal to channel signals in frequency domain; and grouping several frequency bands for each channel signal to form frequency clusters. Further, the method for separating the sound source from the mixed signal includes separating the frequency clusters by applying a blind source separation to signals in frequency domain for each frequency cluster; and integrating the spectrums of the separated signal to restore the sound source in a time domain wherein each of the separated signals expresses one sound source.

Method for separating source signals and apparatus thereof

A Viterbi decoder includes: an observation vector sequence generator for generating an observation vector sequence by converting an input speech to a sequence of observation vectors; a local optimal state calculator for obtaining a partial state sequence having a maximum similarity up to a current observation vector as an optimal state; an observation probability calculator for obtaining, as a current observation probability, a probability for observing the current observation vector in the optimal state; a buffer for storing therein a specific number of previous observation probabilities; a non-linear filter for calculating a filtered probability by using the previous observation probabilities stored in the buffer and the current observation probability; and a maximum likelihood calculator for calculating a partial maximum likelihood by using the filtered probability. The filtered probability may be a maximum value, a mean value or a median value of the previous observation probabilities and the current observation probability.

Viterbi decoder and speech recognition method using same using non-linear filter for observation probabilities

A noise cancellation apparatus includes a noise estimation module for receiving a noise-containing input speech, and estimating a noise therefrom to output the estimated noise; a first Wiener filter module for receiving the input speech, and applying a first Wiener filter thereto to output a first estimation of clean speech; a database for storing data of a Gaussian mixture model for modeling clean speech; and an MMSE estimation module for receiving the first estimation of clean speech and the data of the Gaussian mixture model to output a second estimation of clean speech. The apparatus further includes a final clean speech estimation module for receiving the second estimation of clean speech from the MMSE estimation module and the estimated noise from the noise estimation module, and obtaining a final Wiener filter gain therefrom to output a final estimation of clean speech by applying the final Wiener filter gain.

Noise cancellation system and method

Provided are an apparatus and method for post-processing a dialogue error in a speech dialogue system using multilevel verification, in which both of a user's current utterance and a whole dialogue flow are taken into account through the multilevel verification including speech recognition results analysis, linguistic analysis, discourse analysis and dialogue analysis. As a result, various errors that may occur in the speech dialogue system are detected, and error post-processing appropriate to a detected error type is performed, so that speech recognition errors may be reduced.

Apparatus and method for post-processing dialogue error in speech dialogue system using multilevel verification

An apparatus and method for efficiently constructing learning data required in statistical methodology used in information retrieval, information extraction, translation, natural language processing, etc. are provided. The method includes the steps of: generating learning models by performing machine learning with respect to learning data; attaching tags to a raw corpus automatically by using the generated learning models to thereby generate learning data candidates; calculating confidence scores of the generated learning data candidates, and then selecting a learning data candidate using the confidence scores; and allowing a user to correct an error in the selected learning data candidate through an interface and adding the error-corrected learning data candidate to the learning data, thereby adding new learning models incrementally.

Apparatus and method for constructing learning data

In a semi-automatic construction method for knowledge base of an encyclopedia question answering system, concept-oriented systematic templates are designed and important fact information related to entries is automatically extracted from summary information and body of the encyclopedia to semi-automatically construct the knowledge base of the encyclopedia question answering system. The method includes the steps of: (a) designing structure of the knowledge base with a plurality of templates for each entry and a plurality attributes related to each of the templates; (b) extracting structured information including the entry, an attribute name and attribute values from summary information of the encyclopedia; (c) extracting unstructured information including an attribute name and attribute values of the entry from a body of the encyclopedia; and (d) storing the structured information and the unstructured information in corresponding template and attribute of the knowledge base according to the entry.

Semi-automatic construction method for knowledge base of encyclopedia question answering system

Growing community of inventors

Daejeon, South Korea

Ji Hyun Wang