Abstract: An apparatus for controlling a mobile device according to the present invention includes: a conversation recognition unit configured to recognize a conversation between users through mobile devices; a user intent verification unit configured to verify an intent of at least one user among the users based on the recognition result; and an additional function control unit configured to execute an additional function corresponding to the verified user's intent in a mobile device of the user. According to the present invention, great contribution may be made to improve communication between users by recognizing the conversation between the users, thereby directly providing information associated with the conversation or providing a service.

Abstract: Disclosed herein is a voice-based CAPTCHA method and apparatus which can perform a CAPTCHA procedure using the voice of a human being. In the voice-based CAPTCHA) method, a plurality of uttered sounds of a user are collected. A start point and an end point of a voice from each of the collected uttered sounds are detected and then speech sections are detected. Uttered sounds of the respective detected speech sections are compared with reference uttered sounds, and then it is determined whether the uttered sounds are correctly uttered sounds. It is determined whether the uttered sounds have been made by an identical speaker if it is determined that the uttered sounds are correctly uttered sounds.

Abstract: The present invention relates to automatic summarization so as to recognize entire contents of multimedia data. A method of generating summarized information according to the present invention includes: generating index information on a specific audio signal or a specific video signal among input signals; synchronizing text information extracted from the input signal or received for the input signal with the index information; and generating first summarized information by using the synchronized text information and index information.

Abstract: Disclosed is apparatus and system for user interface. The apparatus for user interface comprises a body unit including a groove which is corresponding to a structure of an oral cavity and operable to be mounted on upper part of the oral cavity; a user input unit receiving a signal from the user's tongue in a part of the body unit; a communication unit transmitting the signal received from the user input unit; and a charging unit supplying an electrical energy generated from vibration or pressure caused by movement of the user's tongue.

Abstract: A transparent display apparatus and a controlling method thereof include a transparent display panel which displays an image, a backlight unit which provides backlight to the transparent display panel, an input unit which receives a user command, a power supply unit which supplies power to the transparent display apparatus, and a controller which controls the power supply unit to cut off power supply to the backlight unit if a preset command is input through the input unit, controls the transparent display panel to output a preset image, and controls the power supply unit to cut off power supply to the transparent display panel after a preset time has elapsed.

Abstract: Method of the present invention may include receiving speech feature vector converted from speech signal, performing first search by applying first language model to the received speech feature vector, and outputting word lattice and first acoustic score of the word lattice as continuous speech recognition result, outputting second acoustic score as phoneme recognition result by applying an acoustic model to the speech feature vector, comparing the first acoustic score of the continuous speech recognition result with the second acoustic score of the phoneme recognition result, outputting first language model weight when the first coustic score of the continuous speech recognition result is better than the second acoustic score of the phoneme recognition result and performing a second search by applying a second language model weight, which is the same as the output first language model, to the word lattice.

Abstract: 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.

Abstract: 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.

Abstract: The present invention includes a hierarchical search process. The hierarchical search process includes three steps. In a first step, a word boundary is determined using a recognition method of determining a following word dependent on a preceding word, and a word boundary detector. In a second step, word unit based recognition is performed in each area by dividing an input voice into a plurality of areas based on the determined word boundary. Finally, in a third step, a language model is applied to induce an optimal sentence recognition result with respect to a candidate word that is determined for each area. The present invention may improve the voice recognition performance, and particularly, the sentence unit based consecutive voice recognition performance.

Abstract: 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.

Abstract: A message service method using speech recognition includes a message server recognizing a speech transmitted from a transmission terminal, generating and transmitting a recognition result of the speech and N-best results based on a confusion network to the transmission terminal; if a message is selected through the recognition result and the N-best results and an evaluation result according to accuracy of the message are decided, the transmission terminal transmitting the message and the evaluation result to a reception terminal; and the reception terminal displaying the message and the evaluation result.

Abstract: A model-based distortion compensating noise reduction apparatus for speech recognition, includes: a speech absence probability calculator for calculating the probability distribution for absence and existence of a speech using the sound absence and existence information for the frames; a noise estimation updater for estimating a more accurate noise component by updating the variance of the clean speech and noise for each frame; and a speech absence probability-based noise filter for outputting a first clean speech through the speech absence probability transmitted from the speech absence probability calculator and a first noise filter. Further, the model-based distortion compensating noise reduction apparatus includes a post probability calculator for calculating post probabilities for mixtures using a GMM containing a clean speech in the first clean speech; and a final filter designer for forming a second noise filter and outputting an improved final clean speech signal using the second noise filter.

Abstract: The present invention relates to a shutter device for camera used for small-sized digital equipment and includes a base forming a light transmission hole to expose a film or charge-coupled device (CCD) to laser beam; an electromagnet mounted on one side of the base; a driving arm which is mounted on one side of the electromagnet on the base so as to pivot on the base; a first shutter and a second shutter to open and close the light transmission hole, which pivots on the part of the base in association with the pivoting of the driving arm; and a magnetic which is installed on the driving arm and has the north magnetic pole and the south magnetic pole at opposite ends of the magnetic or on surfaces opposite to the electromagnet.

Abstract: 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.

Abstract: In order to improve video quality by accurately detecting a preferred color region and correcting the preferred color region to a target color, a method for correcting a color of a display device includes converting input R/G/B data into Y/Cb/Cr data, calculating a weight by an exponential function using a dispersion and a distance between an input value and a target value of the Y/Cb/Cr data, comparing the calculated weight with a target color range so as to detect a target color region, and calculating a detection probability, detecting an average value, a minimum value and a maximum value of the Y/Cb/Cr data for the detected target color region from a previous frame, calculating a correction constant using the average value, the minimum value and the maximum value of the Y/Cb/Cr data, a predetermined target value of the Y/Cb/Cr data, and the Y/Cb/Cr which is detected as the target color region and is input, adding the detection probability to the calculated correction constant as the weight so as to calcul

Abstract: 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.

Abstract: A microphone-array-based speech recognition system using a blind source separation (BBS) and a target speech extraction method in the system are provided. The speech recognition system performs an independent component analysis (ICA) to separate mixed signals input through a plurality of microphone into sound-source signals, extracts one target speech spoken for speech recognition from the separated sound-source signals by using a Gaussian mixture model (GMM) or a hidden Markov Model (HMM), and automatically recognizes a desired speech from the extracted target speech. Accordingly, it is possible to obtain a high speech recognition rate even in a noise environment.

Abstract: Provided are an apparatus and method for generating a noise adaptive acoustic model including a noise adaptive discriminative adaptation method. The method includes: generating a baseline model parameter from large-capacity speech training data including various noise environments; and receiving the generated baseline model parameter and applying a discriminative adaptation method to the generated results to generate an migrated acoustic model parameter suitable for an actually applied environment.

Abstract: An apparatus for evaluating the performance of speech recognition includes a speech database for storing N-number of test speech signals for evaluation. A speech recognizer is located in an actual environment and executes the speech recognition of the test speech signals reproduced using a loud speaker from the speech database in the actual environment to produce speech recognition results. A performance evaluation module evaluates the performance of the speech recognition by comparing correct recognition results answers with the speech recognition results.

Abstract: Disclosed herein are an apparatus and method for recognizing speech. The apparatus includes a frame-based speech recognition unit, a segment division unit, a segment feature extraction unit, a segment speech recognition performance unit, and a combination and synchronization unit. The frame-based speech recognition unit extracts frame speech feature vectors from a speech signal, and performs speech recognition on frames of the speech signal using the frame speech feature vectors and a frame-based probability model. The segment division unit divides the speech signal into segments. The segment feature extraction unit extracts segment speech feature vectors around a boundary between the segments. The segment speech recognition performance unit performs speech recognition on the segments of the speech signal using the segment speech feature vectors and a segment-based probability model.