Yunbin Deng

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A robust speech feature extraction procedure, by kernel regression nonlinear predictive coding, is presented. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions spanning a Reproducing Kernel Hilbert Space (RKHS). Experiments on TI-DIGIT demonstrate consistent robustness of the new features to noise(More)
Parallel isolated word corpora were collected from healthy speakers and individuals with speech impairment due to stroke or cerebral palsy. Surface electromyographic (sEMG) signals were collected for both vocalized and mouthed speech production modes. Pioneering work on disordered speech recognition using the acoustic signal, the sEMG signals, and their(More)
— An auditory perception model for noise-robust speech feature extraction is presented. The model assumes continuous-time filtering and rectification, amenable to real-time, low-power analog VLSI implementation. A 3mm ¢ 3mm CMOS chip in 0.5£ ¥ ¤ CMOS technology implements the general form of the model with digitally programmable filter parameters.(More)
The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech(More)
Real-time classification of objects from active sonar echo-location requires a tremendous amount of computation, yet bats and dolphins perform this task effortlessly. To bridge the gap between human-engineered and biosonar system performance, we developed special-purpose hardware tailored to the parallel distributed nature of the computation performed in(More)
become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms(More)