Chi H. Yim

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In this paper, a new auditory spectrum based speech feature is proposed using sinusoidal representation and auditory model. The feature is optimized using the properties of auditory perception and masking. After quantizing and encoding the optimized feature parameters, a new speech-coding algorithm with average bit-rate of 3.25kbps is developed. The(More)
MFCC are features commonly used in speech recognition systems today. The recognition accuracy of systems using MFCC is known to be high in clean speech environment, but it drops greatly in noisy environment. In this paper, we propose new features called the auditory spectrum based features (ASBF) that are based on the cochlear model of the human auditory(More)
Missing feature theory is well studied in robust ASR context, many works have been done on additive noise of different colors. These are based mainly on classical spectral subtraction and marginal density techniques. This paper addresses the problem of temporal distortion of feature components, that is all about time domain instead of frequency one. No(More)
In statistical speech recognition, misclassification often occurs when there is a mismatch between the incoming signal and the acoustics model inside the recognizer. In order to combat this problem, techniques such as Cepstral Mean Subtraction, Vocal Tract Normalization, adaptation and pronunciation model can be used. In this paper, we proposed a new(More)
This paper presents a parametric matching and smoothing method that is applied to a sinusoidal representation and auditory model-based speech analysis/synthesis system. A 2.6kbps speech-coding algorithm is finally derived based on the speech analysis/synthesis system. The synthetic speech is almost same as that of 3.25kbps speech coding algorithm with(More)
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