• Speech Sciences
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• 제10권2호
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Journal of IKEEE
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• 제2권1호
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• pp.61-67
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• 1998

In this thesis, experiment have performed with the speaker recognition using multilayer feedforward neural network(MFNN) model using Korean and Japanese digits . The 5 adult males and 5 adult females pronounciate form 0 to 9 digits of Korean, Japanese 7 times. And then, they are extracted characteristics coefficient through Pitch deletion algorithm, LPC analysis, and LPC Cepstral analysis to generate input pattern of MFNN. 5 times among them are used to train a neural network, and 2 times is used to measure the performance of neural network. Both Korean and Japanese, Pitch coefficients is about 4%t more enhanced than LPC or LPC Cepstral coefficients.

• The Journal of the Acoustical Society of Korea
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• 제21권3호
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• pp.310-314
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• 2002

The exact pitch (fundamental frequency) extraction is important in speech signal processing like speech recognition, speech analysis and synthesis. However the exact pitch extraction from speech signal is very difficult due to the effect of formant and transitional amplitude. So in this paper, the pitch is detected after the elimination of formant ingredients by flattening the spectrum in frequency region. The effect of the transition and change of phoneme is low in frequency region. In this paper we proposed the new flattening method of log spectrum and the performance was compared with LPC method and Cepstrum method. The results show the proposed method is better than conventional method.

• Phonetics and Speech Sciences
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• 제5권4호
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• pp.33-41
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• 2013

This study investigated the perceptual and cepstral/spectral characteristics of phonation and their relationships in dysarthria in connected speech. Twenty-two participants were divided into two groups; the eleven dysarthric speakers were paired with matching age and gender healthy control participants. A perceptual evaluation was performed by three speech pathologists using the GRBAS scale to measure the cepstrual/spectral characteristics of phonation between the two groups' connected speech. Correlations showed dysarthric speakers scored significantly worse (with a higher rating) with severities in G (overall dysphonia grade), B (breathiness), and S (strain), while the smoothed prominence of the cepstral peak (CPPs) was significantly lower. The CPPs were significantly correlated with the perceptual ratings, including G, B, and S. The utility of CPPs is supported by its high relationship with perceptually rated dysphonia severity in dysarthric speakers. The receiver operating characteristic (ROC) analysis showed that the threshold of 5.08 dB for the CPPs achieved a good classification for dysarthria, with 63.6% sensitivity and the perfect specificity (100%). Those results indicate the CPPs reliably distinguished between healthy controls and dysarthric speakers. However, the CPP frequency (CPP F0) and low-high spectral ratio (L/H ratio) were not significantly different between the two groups.

• The Journal of the Acoustical Society of Korea
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• 제16권1호
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• pp.100-103
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• 1997

In this paper, we analyze the time delayed effect of speech feature. Here, the time delayed effect means that the current feature vector of speech is under the influence of the previous feature vectors. In this paper, we use a set of LPC driven cepstal coefficients and evaluate the time delayed effect of cepstrum with the performance of the speech recognition system. For the experiments, we used the speech database consisting of 22 words which uttered by 50 male speakers. The speech database uttered by 25 male speakers was used for training, and the other set was used for testing. The experimental results show that the time delayed effect is large in the lower orders of feature vector but small in the higher orders.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제41권4호
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• pp.103-111
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• 2004

The method of speaker normalization has been known as the successful method for improving the accuracy of speech recognition at speaker independent speech recognition system. A frequency warping approach is widely used method based on maximum likelihood for speaker normalization. This paper propose a new power spectrum warping approach to making improvement of speaker normalization better than a frequency warping. Th power spectrum warping uses Mel-frequency cepstrum analysis(MFCC) and is a simple mechanism to performing speaker normalization by modifying the power spectrum of Mel filter bank in MFCC. Also, this paper propose the hybrid VTN combined the Power spectrum warping and a frequency warping. Experiment of this paper did a comparative analysis about the recognition performance of the SKKU PBW DB applied each speaker normalization approach on baseline system. The experiment results have shown that a frequency warping is 2.06%, the power spectrum is 3.06%, and hybrid VTN is 4.07% word error rate reduction as of word recognition performance of baseline system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제40권1호
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• pp.74-81
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• 2003

In this study, we'd implemented a real time embedded speech recognition system that requires minimum memory size for speech recognition engine and DB. The word to be recognized consist of 40 commands used in a PCS phone and 10 digits. The speech data spoken by 15 male and 15 female speakers was recorded and analyzed by short time analysis method, which window size is 256. The LPC parameters of each frame were computed through Levinson-Burbin algorithm and they were transformed to Cepstrum parameters. Before the analysis, speech data should be processed by pre-emphasis that will remove the DC component in speech and emphasize high frequency band. Baum-Welch reestimation algorithm was used for the training of HMM. In test phone, we could get a recognition rate using likelihood method. We implemented an embedded system by porting the speech recognition engine on ARM core evaluation board. The overall recognition rate of this system was 95%, while the rate on 40 commands was 96% and that 10 digits was 94%.

• The Journal of the Acoustical Society of Korea
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• 제39권4호
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• pp.292-302
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• 2020

Machine learning algorithms have made immense contributions in various fields including sonar and radar applications. Recently developed Cycle-Consistency Generative Adversarial Network (CycleGAN), a variant of GAN has been successfully used for unpaired image-to-image translation. We present a modified CycleGAN for translation of underwater ship engine sounds with high perceptual quality. The proposed network is composed of an improved generator model trained to translate underwater audio from one vessel type to other, an improved discriminator to identify the data as real or fake and a modified cycle-consistency loss function. The quantitative and qualitative analysis of the proposed CycleGAN are performed on publicly available underwater dataset ShipsEar by evaluating and comparing Mel-cepstral distortion, pitch contour matching, nearest neighbor comparison and mean opinion score with existing algorithms. The analysis results of the proposed network demonstrate the effectiveness of the proposed network.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• 제14권2호
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• pp.110-116
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• 2003

Background and Objectives : Laryngeal cancer discrimination using voice signals is a non-invasive method that can carry out the examination rapidly and simply without giving discomfort to the patients. n appropriate analysis parameters and classifiers are developed, this method can be used effectively in various applications including telemedicine. This study examines voice analysis parameters used for laryngeal disease discrimination to help discriminate laryngeal diseases by voice signal analysis. The study also estimates the laryngeal cancer discrimination activity of the Gaussian mixture model (GMM) classifier based on the statistical modelling of voice analysis parameters. Materials and Methods : The Multi-dimensional voice program (MDVP) parameters, which have been widely used for the analysis of laryngeal cancer voice, sometimes fail to analyze the voice of a laryngeal cancer patient whose cycle is seriously damaged. Accordingly, it is necessary to develop a new method that enables an analysis of high reliability for the voice signals that cannot be analyzed by the MDVP. To conduct the experiments of laryngeal cancer discrimination, the authors used three types of voices collected at the Department of Otorhinorlaryngology, Pusan National University Hospital. 50 normal males voice data, 50 voices of males with benign laryngeal diseases and 105 voices of males laryngeal cancer. In addition, the experiment also included 11 voices data of males with laryngeal cancer that cannot be analyzed by the MDVP, Only monosyllabic vowel /a/ was used as voice data. Since there were only 11 voices of laryngeal cancer patients that cannot be analyzed by the MDVP, those voices were used only for discrimination. This study examined the linear predictive cepstral coefficients (LPCC) and the met-frequency cepstral coefficients (MFCC) that are the two major cepstrum analysis methods in the area of acoustic recognition. Results : The results showed that this met frequency scaling process was effective in acoustic recognition but not useful for laryngeal cancer discrimination. Accordingly, the linear frequency cepstral coefficients (LFCC) that excluded the met frequency scaling from the MFCC was introduced. The LFCC showed more excellent discrimination activity rather than the MFCC in predictability of laryngeal cancer. Conclusion : In conclusion, the parameters applied in this study could discriminate accurately even the terminal laryngeal cancer whose periodicity is disturbed. Also it is thought that future studies on various classification algorithms and parameters representing pathophysiology of vocal cords will make it possible to discriminate benign laryngeal diseases as well, in addition to laryngeal cancer.

• Journal of Broadcast Engineering
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• 제1권2호
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• pp.85-95
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• 1996

The purpose of this paper Is to propose a dubbed signal time-synchroniztion technique based on the SOLA(Synchronized Over-Lap and Add) method which has been widely used to modify the time scale of speech signal. In broadcasting audio recording environments, the high degree of background noise requires dubbing process. Since the time difference between the original and the dubbed signal ranges about 200mili seconds, process is required to make the dubbed signal synchronize to the corresponding image. The proposed method finds he starting point of the dubbing signal using the short-time energy of the two signals. Thereafter, LPC cepstrum analysis and DTW(Dynamic Time Warping) process are applied to synchronize phoneme positions of the two signals. After determining the matched point by the minimum mean square error between orignal and dubbed LPC cepstrums, the SOLA method is applied to the dubbed signal, to maintain the consistency of the corresponding phase. Effectiveness of proposed method is verified by comparing the waveforms and the spectrograms of the original and the time synchronized dubbing signal.