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: A method of providing targeted advertisements, includes: generating an advertisement search result using a search query based on a user query or a recommended query; generating an advertisement search result on the basis of a user profile; integrating the advertisement search result generated on the basis of the user profile with the advertisement search result generated by using the search query; and presenting a recommended advertisement list on the basis of the integrated advertisement search result and advertisement information exposed to a user.

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 metadata search apparatus using speech recognition includes a metadata processor for processing contents metadata to obtain allomorph of target vocabulary required for speech recognition and search; a metadata storage unit for storing the contents metadata; a speech recognizer for performing speech recognition on speech data uttered by a user by searching the allomorph of the target vocabulary; a query language processor for extracting a keyword from the vocabulary speech-recognized by the speech recognizer; and a search processor for searching the metadata storage unit to extract the contents metadata corresponding to the keyword. An IPTV receiving apparatus employs the metadata search apparatus to provide IPTV services through the functions of speech recognition.

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: 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: 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: According to a method and apparatus for speech recognition in noise environment of the present invention using uncertainty information for sub-band, uncertainty information of each sub-band is extracted from estimated clean speech using noise modeling, and helps to extract speech features that are robust to noise using the extracted uncertainty information as a weight with respect to each sub-band. Also, an acoustic model is converted according to each sub-band weight, and speech recognition is performed based on the converted acoustic model and the extracted speech features. As a result, while the noise modeling over time is not so accurate, noise influence resulted from sub-bands having high corruption can be reduced according to the uncertainty information of the corresponding sub-band, and speech recognition performance in complex noise environments can be improved.

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.