Mirror neuron system （MNS） represents one past decade, and it has been found to involve in multiple of the most important discoveries of cognitive neuroscience in the aspects of brain functions including action unde...Mirror neuron system （MNS） represents one past decade, and it has been found to involve in multiple of the most important discoveries of cognitive neuroscience in the aspects of brain functions including action understanding, imitation, language understanding, empathy, action prediction and speech evolution. This manuscript reviewed the function of MNS in action understanding as well as language evolution, and specifically assessed its roles as the bridge from body language to fluent speeches. Then we discussed the speech defects of autism patients due to the disruption of MNS. Finally, given that MNS is plastic in adult brain, we proposed MNS targeted therapy provides an efficient rehabilitation approach for brain damages conditions as well as autism patients.展开更多
To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empi...To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.展开更多
基金Sci-ence Foundation of Ministry of Education of China (No.FBB011469)
文摘Mirror neuron system （MNS） represents one past decade, and it has been found to involve in multiple of the most important discoveries of cognitive neuroscience in the aspects of brain functions including action understanding, imitation, language understanding, empathy, action prediction and speech evolution. This manuscript reviewed the function of MNS in action understanding as well as language evolution, and specifically assessed its roles as the bridge from body language to fluent speeches. Then we discussed the speech defects of autism patients due to the disruption of MNS. Finally, given that MNS is plastic in adult brain, we proposed MNS targeted therapy provides an efficient rehabilitation approach for brain damages conditions as well as autism patients.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60475016)the Foundational Research Fund of Harbin Engineering University (Grant No.HEUF04092)
文摘To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.
