• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.60-65
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• 2002

In this paper, we propose GFCC(gammatone filter frequency cepstrum coefficient) parameter which was based on the auditory characteristic for accomplishing better speech recognition rate. And it is performed the experiment of speech recognition for isolated word acquired from telephone network. For the purpose of comparing GFCC parameter with other parameter, the experiment of speech recognition are carried out using MFCC and LPCC parameter. Also, for each parameter, we are implemented CMS(cepstral mean subtraction)which was applied or not in order to compensate channel distortion in telephone network. Accordingly, we found that the recognition rate using GFCC parameter is better than other parameter in the experimental result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1307-1311
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• 2006

In this paper, we propose new speech feature parameter using the Matrix Factorization for appearance part-based features of speech spectrum. The proposed parameter represents effective dimensional reduced data from multi-dimensional feature data through matrix factorization procedure under all of the matrix elements are the non-negative constraint. Reduced feature data presents p art-based features of input data. We verify about usefulness of NMF(Non-Negative Matrix Factorization) algorithm for speech feature extraction applying feature parameter that is got using NMF in Mel-scaled filter bank output. According to recognition experiment results, we confirm that proposed feature parameter is superior to MFCC(Mel-Frequency Cepstral Coefficient) in recognition performance that is used generally.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.359-364
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• 2004

In this paper, we implemented real-time speaker undependent speech recognizer that is robust in noise environment using DSP(Digital Signal Processor). Implemented system is composed of TMS320C32 that is floating-point DSP of Texas Instrument Inc. and CODEC for real-time speech input. Speech feature parameter of the speech recognizer used robust feature parameter in noise environment that is transformed feature space of MFCC(met frequency cepstral coefficient) using ICA(Independent Component Analysis) on behalf of MFCC. In recognition result in noise environment, we hew that recognition performance of ICA feature parameter is superior than that of MFCC.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.142-145
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• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of it is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDs with different ranges of candidate frames. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.3
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• pp.381-386
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• 1987

By the speech production model, the first positive peak in a pitch interval of the voiced speech is mainly affected by the glottis and the first formant component, known as a typical energy source of the voiced speech. From these characteristics, the energy parameter can be replaced by the area of the area of the positve peak in a pitch interval, which parameter is generally used for classification of speech signals. In this method, the changed energy parameter is independent of window length applied for analysis, and the pitch can be extracted smultaneously. Furthermore, the energy can be extracted in the pitch period unit.

• MALSORI
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• v.55
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• pp.119-130
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• 2005

The aim of this paper is to investigate some acoustical characteristics of the voice quality features from the emotional speech database. Six different parameters are measured and compared for 6 different emotions (normal, happiness, sadness, fear, anger, boredom) and from 6 different speakers. Inter-speaker variability and intra-speaker variability are measured. Some intra-speaker consistency of the parameter change across the emotions are observed, but inter-speaker consistency are not observed.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.91-94
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• 1994

This paper is a study on the design and implementation of the Korean Tet-to-Speech system. In this paper, parameter symthesis method is chosen for speech symthesis method and PARCOR coeffient, one of the LPC analysis, is used as acoustic parameter, We use a diphone as synthesis unit, it include a basic naturalness of human speech. Diphone DB is consisted of 1228 PCM files. LPC synthesis method has defect that decline clearness of synthesis speech, during synthesizing unvoiced sound In this paper, we improve clearness of synthesized speech, using residual signal as ecitation signal of unvoiced sound. Besides, to improve a naturalness, we control the prosody of synthesized speech through controlling the energy and pitch pattern. Synthesis system is implemented at PC/486 and use a 70Hz-4.5KHz band pass filter for speech imput/output, amplifier and TMS320c30 DSP board.

• Speech Sciences
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• v.11 no.2
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• pp.247-257
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• 2004

There has been many research efforts to overcome the problems of speech recognition in the noisy conditions. Among them, the model-based compensation methods such as the parallel model combination (PMC) and vector Taylor series (VTS) have been found to perform efficiently compared with the previous speech enhancement methods or the feature-based approaches. In this paper, a data-driven model compensation approach that adapts the HMM(hidden Markv model) parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional model-based methods such as the PMC, the statistics necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1673-1678
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• 2016

In the area of the speech recognition processing, background noises are caused the incorrect response to the speech input, therefore the speech recognition rates are decreased by the background noises. Accordingly, a more high level noise processing techniques are required since these kinds of noise countermeasures are not simple. Therefore, this paper proposes an algorithm to distinguish between the stationary background noises or non-stationary background noises and the speech signal having short time duration in the noisy environments. The proposed algorithm uses the characteristic parameter of the improved speech signal as an important measure in order to distinguish different types of the background noises and the speech signals. Next, this algorithm estimates various kinds of the background noises using a multi-layer perceptron neural network. In this experiment, it was experimentally clear the estimation of the background noises and the speech signals.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1107-1110
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• 1999

In speech signal processing, the accurate decision of the voiced/unvoiced sound is important for robust word recognition and analysis and a high coding efficiency. In this paper, we propose the mehod of the voiced/unvoiced decision using the LSP parameter which represents the spectrum characteristics of the speech signal. The voiced sound has many more LSP parameters in low frequency region. To the contrary, the unvoiced sound has many more LSP parameters in high frequency region. That is, the LSP parameter distribution of the voiced sound is different to that of the unvoiced sound. Also, the voiced sound has the minimun value of sequantial intervals of the LSP parameters in low frequency region. The unvoiced sound has it in high frequency region. we decide the voiced/unvoiced sound by using this charateristics. We used the proposed method to some continuous speech and then achieved good performance.