Electronics and Telecommunications Research Institute

Samsung Electronics Co., Ltd.

Ki-young Park

Jeon Gue Park

Hoon Chung

Byung Ok Kang

Sung Joo Lee

Ji Hyun Wang

Jeom Ja Kang

Jong Jin Kim

Ho-Young Jung

Hyung-Bae Jeon

Yunkeun Lee

Euisok Chung

Chang-kyu Choi

Yun Keun Lee

Eui Sok Chung

Kwang-cheol Oh

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 method and an apparatus are provided for discriminating between a voice region and a non-voice region in an environment in which diverse types of noises and voices exist. The voice discrimination apparatus includes a domain transform unit for transforming an input sound signal frame into a frame in the frequency domain, a model training/update unit for setting a voice model and a plurality of noise models in the frequency domain and initializing or updating the models, a speech absence probability (SAP) computation unit for obtaining a SAP computation equation for each noise source by using the initialized or updated voice model and noise models and substituting the transformed frame into the equation to compute an SAP for each noise source, a noise source selection unit for selecting the noise source by comparing the SAPs computed for the respective noise sources, and a voice judgment unit for judging whether the input frame corresponds to the voice region in accordance with the SAP level of the selected noise source.

Method and apparatus for discriminating between voice and non-voice using sound model

A method and apparatus for distinguishing a voice region from a non-voice region in an environment where various types of noise and voice are mixed together are provided. The method includes the steps of converting an input voice signal into a frequency domain signal by preprocessing the input voice signal, performing sigmoid compression on the converted signal, transforming a spectrum vector generated by the sigmoid compression into a voice detection parameter in scalar form, and detecting the voice region using the parameter.

Method and apparatus for detecting voice region

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Daejeon, South Korea

Ki-young Park