Inference in Switching Linear Dynamical Systems Applied to Noise Robust Speech Recognition of Isolated Digits
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Integrated Hall devices have the great advantage, over other magnetic sensors, that they can be fully fabricated by a standard CMOS process. However they are known to have a relatively large offset (i.e. residual voltage at zero magnetic field). Techniques ...
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This paper demonstrates the robustness of group delay based features to additive noise. First, we analytically show the robustness of group delay based represen- tations. The analysis makes use of the fact that, for minimum-phase signals, the group delay f ...
We address the issue of noise robustness of reconstruction techniques for frequency-domain optical-coherence tomography (FDOCT). We consider three reconstruction techniques: Fourier, iterative phase recovery, and cepstral techniques. We characterize the re ...
Institute of Electrical and Electronics Engineers2010
Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each ...
Cochlear implant-like spectrally reduced speech (SRS) has previously been shown to afford robustness to additive noise. In this paper, it is evaluated in the context of microphone array based automatic speech recognition (ASR). It is compared to and combin ...
The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a novel approach where a set of client-specific binary features carrying maximal di ...
Adaptive networks, consisting of a collection of nodes with learning abilities, are well-suited to solve distributed inference problems and to model various types of self-organized behavior observed in nature. One important issue in designing adaptive netw ...
In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, where the object instances are 1-D short-time spectral vectors obtained from the speech signal. More precisely, we investigate the ge ...