• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.492-496
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• 2012

This paper presents a decoding technique for speech recognition using uncertainty information from feature compensation method to improve the speech recognition performance in the low SNR condition. Traditional feature compensation algorithms have difficulty in estimating clean feature parameters in adverse environment. Those algorithms focus on the point estimation of desired features. The point estimation of feature compensation method degrades speech recognition performance when incorrectly estimated features enter into the decoder of speech recognition. In this paper, we apply the uncertainty information from well-known feature compensation method, such as IMM, to the recognition engine. Applied technique shows better performance in the Aurora-2 DB.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• ETRI Journal
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• v.20 no.1
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• pp.16-27
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• 1998

In this paper, we consider the acquisition performance of an IS-95 reverse link access channel slot as a function of system design parameters such as postdetection integration length and the number of access channel message block repetitons. The uncertainty region of the reverse link spreading codes compared to that of forward link is very small, since the uncertainty region of the reverse link is determined by a cell radius. Thus, the parallel acquisiton technique in the reverse link is more efficient than a serial acquisition technique in terms of implementation and of acquisition time. The parallel acquisition is achieved by a bank of N parallel I/Q noncoherent correlator are analyzed for band-limited noise and the Rayleigh fast fading channel. The detection probability is derived for multiple correct code-phase offsets and multipath fading. The probability of no message error is derived when rake combining, access channel message block combining, and Viterbi decoding are applied. Numerical results provide the acquisition performance for system design parameters such as postdetection integration length and number of access channel message block repetitions in case of a random access on a mobile station.