Sung-Joo Ahn

Raised serum interleukin 15 levels in Kawasaki disease

Background: Interleukin (IL)15 is a novel cytokine that induces T cell proliferation, B cell maturation, natural killer cell cytotoxicity, and may have a pivotal role in the pathogenesis of inflammatory disease, acting upstream from tumour necrosis factor α (TNFα). Kawasaki disease (KD) is an inflammatory disease, in which serum levels of inflammatory cytokines such as TNFα and IL6 are increased. Objective: To examine the serum levels of IL15 in KD and to evaluate the role of IL15 in estimating the severity of inflammation in KD. Results and conclusion: There was a significant increase in the mean (SD) serum levels of IL15 measured in the acute stage of KD (11.5 (5.8) pg/ml) compared with those in the subacute stage (1.3 (0.9) pg/ml) (p<0.01) and normal controls (0.9 (1.0) pg/ml) (p<0.01). The increase in IL15 correlated with the increase in TNFα (rs=0.66, p<0.01); however it did not correlate with the levels of erythrocyte sedimentation rate and C reactive protein, suggesting that IL15 may not be a useful marker in estimating the severity of inflammation in KD.

Background noise reduction via dual-channel scheme for speech recognition in vehicular environment

This work concerns an effective dual-channel noise reduction method to increase the performance of robust speech recognition in a vehicular environment. While various single channel methods have already been developed and dual-channel methods have been studied somewhat, their effectiveness in real environments, such as in vehicular, has not yet been formally proven in terms of achieving an acceptable performance level. Our aim is to remedy the low performance of the single and dual-channel noise reduction methods. In particular, we propose a dual-channel noise reduction method based on a high-pass filter and front-end processing, using the eigendecomposition method. Representative experiments were conducted with a real multichannel car corpus and results were compared with respect to the microphone arrangements. From the analysis and results, we show that the enhanced eigendecomposition method, combined with high-pass filter, indeed significantly improves the speech recognition performance under a dual-channel environment.

Combining acoustic echo cancellation and adaptive beamforming for achieving robust speech interface in mobile robot

This paper proposes a combined scheme in which adaptive beamforming and acoustic echo canceller are integrated in order to achieve a robust full-duplex speech interface between user and mobile robot. In particular, this paper addresses the situation in which an echo signal and the userpsilas voice are propagated from the same direction toward a linear array of microphones. We propose a cascading scheme that uses an acoustic echo canceller followed by an adaptive beamformer. By using the system output to adapt echo canceller, and by controlling the adaptive mode of each adaptive filter in the beamformer and in the echo canceller, we can recover the original speech, which would otherwise have remained contaminated by both noise and echoes. In addition, a double-talk detector is proposed so that effective acoustic echo cancellation may be attained without generating error divergence when the userpsilas voice and the echo are present simultaneously. Representative experimental results with real data demonstrate the validity of the proposed scheme.

Effective speaker adaptations for speaker verification

This paper concerns effective speaker adaptation methods to solve the over-training problem in speaker verification, which frequently occurs when modeling a speaker with sparse training data. While various speaker adaptations have already been applied to speech recognition, these methods have not yet been formally considered in speaker verification. This paper proposes speaker adaptation methods using a combination of maximum a posteriori (MAP) and maximum likelihood linear regression (MLLR) adaptations, which are successfully used in speech recognition, and applies to speaker verification. Our aim is to remedy the small training data problem by investigating effective speaker adaptations for speaker modeling. Experimental results show that the speaker verification system using a weighted MAP and MLLR adaptation outperforms that of the conventional speaker models without adaptation by a factor of up to 5 times. From these results, we show that the speaker adaptation method achieves significantly better performance even when only small training data is available for speaker verification.

Enabling directional human-robot speech interface via adaptive beamforming and spatial noise reduction

This paper introduces a home robot application of multi-channel based spatial noise reduction for creating human-robot speech interfaces. A microphone array is employed first to create a speech-only directional conduit, which is realized through adaptive beamforming. Through the directional conduit, the intended speech signal from the desired direction is processed for detection and recognition, while unintended speech-like-sources or undesirable noise from other angles is suppressed. If speech signal is absent among the incoming signals through the conduit, further attenuation of undesirable signals is achieved by using a spatial noise reduction filter. Experimental validation of the technique was conducted using a computer simulation and also an online Samsung AnyBot test. Although the environments exhibited highly non-stationary noise, the method achieved an average speech recognition rate of 87.4% in the case of the computer simulation and 81.6% for the online Samsung AnyBot test. From the cases tested so far, the proposed implementation seems to be effective for practical robot applications in highly non-stationary noise environment.