• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.117-124
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• 2011

This study investigated the effects of the types of noise and signal to noise ratios (SNRs) on speech intelligibility of an adult with dysartrhia. Speech intelligibility was judged by 48 naive listeners using a word transcription task. Repeated measures design was used with the types of noise (multi-talker babble/environmental noise) and SNRs (0, +10 dB, +20 dB) as within-subject factors. The dependent measure was the percentage of correctly transcribed words. Results revealed that two main effects were statistically significant. Listeners performed significantly worse in the multi-talker babble condition than the environmental noise condition, and they performed significantly better at higher levels of SNRs. The current results suggested that the multi-talker babble and lower level of SNRs decreased the speech intelligibility of adults with dysarthria, and speech-language pathologists should consider environmental factors such as the types of noise and SNRs in evaluating speech intelligibility of adults with dysarthria.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.6
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• pp.1819-1830
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• 1991

The problems associated with noise/energy source identification using multiple input/single output model in linear systems are investigated. Partial coherence function is formulated for the model introducing a virtual force and extraneous noises into the conventional two input/single output system. The analytical results show that the partial coherence function in two input/single output linear system is the function of noise/signal ratios when multiple inputs are mutually coherent and extraneous noises exist. Parametric studies for ordinary and partial coherence functions are carried out to demonstrate the effects of noise/signal ratios for these functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• 한국초전도학회:학술대회논문집
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• 2007.07a
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• pp.61-61
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• 2007

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.610-621
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• 1998

In parameter design introduced by Taguchi, we analyze a signal-to-noise(SN) ratio. The SN ratio is a function of the expected loss due to the variation of quality characteristic. In this paper, an easy way for developing SN ratios is presented, which can be used to several quality characteristics simultaneously in parameter design. To develop such multivariate SN ratios, the transformation method of the expected loss and combining techniques are employed. And the analysis of real empirical data for an application of the proposed method is also presented.

• Journal of Korean Society for Quality Management
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• v.29 no.4
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• pp.82-91
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• 2001

Taguchi robust design is widely used to quality improve methods. Especially, interest of dynamic characteristics parameter design is getting grow. In this paper, we have a comparative analyzed three Signal-to-Noise Ratios which are used in American Supplier Institute(ASI), MINITAB and Taguchi series published by Japanese Standard Institute with numerical examples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.72-81
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• 2003

The purpose of this paper is to propose an interactive method, which is designed to select the optimal preferred alter-native for the MLDM(Multiple-the Larger-the better type Decision-Making) problems with the-larger-the-better quality characteristics. The basic idea of the paper is essentially to eliminate inefficient alternative based on the concept of Taguchi Signal-to-Noise ratios and the cutting range instead of using UVF(Utility/value Function) on the group of attributes that can be considered importantly by the decision makers. As a result, the method proposed in the paper for MLDM problems can be significant in that the change of characteristics is transformed into the size of Signal-to-Noise ratio, which can be relatively easy to understand by decision makers.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.421-424
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• 2008

This paper proposes an adaptive noise suppression system based on human auditory model to enhance speech signal that is degraded by various background noises. The proposed system detects voiced and unvoiced sections for each frame and implements the adaptive auditory process, then reduces the noise speech signal using neural network including amplitude component and phase component. Base on measuring signal-to-noise ratios, experiments confirm that the proposed system is effective for speech signal that is degraded by various noises.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.652-657
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• 2009

This paper proposes an adaptive noise suppression system based on human auditory model to enhance speech signal that is degraded by various background noises. The proposed system detects voiced, unvoiced and silence sections for each frame and implements an adaptive auditory process, then reduces the noise speech signal using a neural network including amplitude component and phase component. Based on measuring signal-to-noise ratios, experiments confirm that the proposed system is effective for speech signal that is degraded by various noises.