• Speech Sciences
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• v.8 no.3
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• pp.93-104
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• 2001

The author analyzed acoustic parameters of ten Korean numbers produced by ten male students using Praat. Variations of f0, F1, F2 and F3 within and between speakers were examined by determining an average and standard deviation of the parameters of each number and by comparing the acoustic values with one another. Results showed that each subject produced the numbers within a certain range of variation across time. Thus, speaker identification can be more certain using dynamic information of the acoustic parameters within each vocalic segment. Also, percent difference of within-subjects' variation to that of between-subjects can be utilized to determine which sounds would be better stimuli for speaker identification. According to the criteria, the number '2' proved the best stimulus while the number '7' was the worst. Future studies will be necessary to explore robust methods of speaker identification under noisy conditions.

• Journal of Internet Computing and Services
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• v.4 no.3
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• pp.9-14
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• 2003

The method of vocal tract normalization is a successful method for improving the accuracy of inter-speaker normalization. In this paper, we present an intra-speaker warping factor estimation based on pitch alteration utterance. The feature space distributions of untransformed speech from the pitch alteration utterance of intra-speaker would vary due to the acoustic differences of speech produced by glottis and vocal tract. The variation of utterance is two types: frequency and amplitude variation. The vocal tract normalization is frequency normalization among inter-speaker normalization methods. Therefore, we have to consider amplitude variation, and it may be possible to determine the amplitude warping factor by calculating the inverse ratio of input to reference pitch. k, the recognition results, the error rate is reduced from 0.4% to 2.3% for digit and word decoding.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.69-75
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• 2001

Intra-speaker variation, noise environments, and mismatch between training and test conditions are the major reasons for the speaker verification system unable to use it practically. In this study, we propose robust end-point detection algorithm, noise cancelling with the microphone property compensation technique, and inter-speaker discriminate technique by weighting cepstrum for robust speaker verification system. Simulation results show that the average speaker verification rate is improved in the rate of 17.65% with proposed end-point detection algorithm using LPC residue and is improved in the rate of 36.93% with proposed noise cancelling and microphone property compensation algorithm. The proposed weighting function for discriminating inter-speaker variations also improves the average speaker verification rate in the rate of 6.515%.

• Speech Sciences
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• v.15 no.4
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• pp.7-18
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• 2008

The purpose of the present study is to test whether VOT values of the Korean plain stops in intervocalic position are encoded differently by individual speakers. In Scobbie (2006), the VOT values to the /p/-/b/ voicing contrast in Shetland Isles English were found to demonstrate a high degree of inter-speaker variation. More importantly such variation was not arbitrary: first, there was an inverse relationship between the amount of prevoicing for /b/ and the duration of aspiration for /p/. Second, the inter-speaker variation was shown to be similar between the subjects and their parents. These results suggest that the phonetic targets for VOT are specified in fine detail by speakers. The present study further explores this issue in terms of testing 1) whether the likelihood and the amount of voicing for the intervocalic plain stops in Korean show inter-speaker variation; 2) whether the likelihood and the exact amount of voicing for the intervocalic plain stops in Korean are closely related to the amount of aspiration for the Korean intervocalic aspirated stops. The results of the study suggest that the voicing of intervocalic plain stops in Korean varied according to the individual speakers, but it did not seem to be directly interrelated with the amount of aspiration of the aspirated stop sin the same phonological position.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.803-804
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• 2012

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.48-53
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• 2001

In speaker verification based on LPC analysis the prediction residues are ignored and LPCC(LPC cepstrum) are only used to compose feature vectors. In this study, LPCC and RCEP (residual cepstrum) extracted from residues are used as feature parameters in the various environmental speaker verification. We propose the weighting function which can enlarge inter-speaker variation by weighting pitch, speaker inherent vector, included in residual cepstrum. Simulation results show that the average speaker verification rate is improved in the rate of 6％ with RCEP and LPCC at the same time and is improved in the rate of 2.45% with the proposed weighted RCEP and LPCC at the same time compared with no weighting.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.410-415
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• 2005

Recently, most of the speaker verification systems are based on the pattern recognition approach method. And performance of the pattern-classifier depends on how to classify a variety of speakers' feature parameters. In order to classify feature parameters efficiently and effectively, it is of great importance to enlarge variations between speakers and effectively measure distances between feature parameters. Therefore, this paper would suggest the positively mixed model scheme that can enlarge inter-speaker variation by searching the individual model with world model at the same time. During decision procedure, we can maximize inter-speaker variation by using the proposed mixed model scheme. We also make use of a symbol probability weighting function in this system so as to reduce vector quantization errors by measuring symbol probability derived from the distance rate of between the world codebook and individual codebook. As the result of our experiment using this method, we could halve the Detection Cost Function (DCF) of the system from $2.37\%\;to\;1.16\%$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.257-267
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• 2009

This paper presents a training method for neural networks and the employment of MSE (mean scare error) values as the basis of a decision regarding the identity claim of a speaker in a recurrent neural networks based speaker verification system. Recurrent neural networks (RNNs) are employed to capture temporally dynamic characteristics of speech signal. In the process of supervised learning for RNNs, target outputs are automatically generated and the generated target outputs are made to represent the temporal variation of input speech sounds. To increase the capability of discriminating between the true speaker and an impostor, a discriminative training method for RNNs is presented. This paper shows the use and the effectiveness of the MSE value, which is obtained from the Euclidean distance between the target outputs and the outputs of networks for test speech sounds of a speaker, as the basis of speaker verification. In terms of equal error rates, results of experiments, which have been performed using the Korean speech database, show that the proposed speaker verification system exhibits better performance than a conventional hidden Markov model based speaker verification system.

• MALSORI
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• no.56
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• pp.147-158
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• 2005

In this paper, we are focused to develop the simplified speaker verification algorithm without background speaker models, which will be adopted in the portable speaker verification system equipped in portable terminals such as mobile phone and PMP. According to the tolerance interval analysis, the population of someone's speaker model can be represented by a suitable number of selected independent samples of speaker model. So we can make the representative speaker model and threshold under the specified confidence level and coverage. Using proposed algorithm with the number of samples is 40, the experiments show that the false rejection rate is $3.0\%$ and the false acceptance rate $4.3\%$, worth comparing to conventional method's results, $5.4\%\;and\;5.5\%$, respectively. Next step of research will be on the suitable adaptation methods to overcome speech variation problems due to aging effect and operating environments.

• Phonetics and Speech Sciences
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• v.9 no.2
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• pp.77-83
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• 2017

We propose a new method to reduce emotion recognition errors caused by variation in speaker characteristics and speech rate. Firstly, for reducing variation in speaker characteristics, we adjust features from a test speaker to fit the distribution of all training data by using the histogram equalization (HE) algorithm. Secondly, for dealing with variation in speech rate, we augment the training data with speech generated in various speech rates. In computer experiments using EMO-DB, KRN-DB and eNTERFACE-DB, the proposed method is shown to improve weighted accuracy relatively by 34.7%, 23.7% and 28.1%, respectively.