• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2521-2526
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• 2011

This paper proposes a speaker recognition algorithm using a perceptron neural network and LPC (Linear Predictive Coding) cepstrum coefficients. The proposed algorithm first detects the voiced sections at each frame. Then, the LPC cepstrum coefficients which have speaker characteristics are obtained by the linear predictive analysis for the detected voiced sections. To classify the obtained LPC cepstrum coefficients, a neural network is trained using the LPC cepstrum coefficients. In this experiment, the performance of the proposed algorithm was evaluated using the speech recognition rates based on the LPC cepstrum coefficients and the neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.775-780
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• 2013

This paper proposes a speaker-dependent speech recognition algorithm which can classify the gender for male and female speakers in white noise and car noise, using a neural network. The proposed speech recognition algorithm is trained by the neural network to recognize the gender for male and female speakers, using LPC (Linear Predictive Coding) cepstrum coefficients. In the experiment results, the maximal improvement of total speech recognition rate is 96% for white noise and 88% for car noise, respectively, after trained a total of six neural networks. Finally, the proposed speech recognition algorithm is compared with the results of a conventional speech recognition algorithm in the background noisy environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.66-71
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• 2008

Recent multimedia technology has supported various application services based on the development of effective movie compression and network techniques. On-line video conference system is a typical example that use theses two technologies effectively. On-line video conference system can be characterized into an effective conferencing method for long-distanced on-line conference members. But, unfortunately, off-line conference with face-to-face meeting is more frequent than on-line conference and their support systems have not been sufficiently considered. In this paper, we propose a recording support system for off-Line conference using face and speaker recognition. This system finds the speaker in the conference by using three microphones and three webcam cameras. And analysis is done with face region information that gathered by currently active webcam camera, and recognizes the identity of face. Finally, the system tracks speaker and records conference with extract speaker information.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.190-198
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• 2002

In general, we have used the Viterbi algorithm of Speech recognition for decoding. But a decoder in speaker verification has to recognize same word of every speaker differently. In this paper, we propose a noble decoding algorithm that could replace the typical Viterbi algorithm for the speaker verification system. We utilize for the proposed algorithm the speaker adaptation algorithms that transform feature vectors into the region of the client' characteristics in the speech recognition. There are many adaptation algorithms, but we take MLLR (Maximum Likelihood Linear Regression) and MAP (Maximum A-Posterior) adaptation algorithms for proposed algorithm. We could achieve improvement of performance about 30% of EER (Equal Error Rate) using proposed algorithm instead of the typical Viterbi algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2402-2409
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• 2011

The voice has distinguishable acoustic properties of speaker as well as transmitting information. The speaker recognition is the method to figures out who speaks the words through acoustic differences between speakers. The speaker recognition is roughly divided two kinds of categories: speaker verification and identification. The speaker verification is the method which verifies speaker himself based on only one's voice. Otherwise, the speaker identification is the method to find speaker by searching most similar model in the database previously consisted of multiple subordinate sentences. This paper composes feature vector from extracting MFCC coefficients and uses the dynamic time warping algorithm to compare the similarity between features. In order to describe common characteristic based on phonological features of spoken words, two subordinate sentences for each speaker are used as the training data. Thus, it is possible to identify the speaker who didn't say the same word which is previously stored in the database.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.146-156
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• 1989

This paper discusses how to choose speech parameters and decision logics to improve the performance of speaker recognition systems. It also considers the influence of the reference patterns on the speaker recognition. It is observed from the performance analysis based on LPSs, PARCOR coefficients and LPC-cepstrum coefficients that LPC-cepstrum coefficients are superior to the others in speaker recognition without regard to the reference patterns. In order to improve the recognition performance, a new decision logic is proposed based on a generalized-distance concept. It differs from the existing methods in that it considers the statistics of customer and impostors at the same time. It turns out from a speaker verification test that the proposed decision logic ferforms better than the existing ones.

• The Journal of the Acoustical Society of Korea
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• v.10 no.4
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• pp.27-37
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• 1991

This paper proposes the ESFVQ(energy subspace fuzzy vector quantization) that employs energy subspaces to reduce the quantizing distortion which is less than that of a fuzzy vector quatization. The ESFVQ is applied to a speaker adaptation method by which Korean words spoken by unknown speakers are recognized. By generating mapped codebooks with fuzzy histogram according to each energy subspace in the training procedure and by decoding a spoken word through the ESFVQ in the recognition proecedure, we attempt to improve the recognition rate. The performance of the ESFVQ is evaluated by measuring the quantizing distortion and the speaker adaptive recognition rate for DDD telephone area names uttered by 2 males and 1 female. The quatizing distortion of the ESFVQ is reduced by 22% than that of a vector quantization and by 5% than that of a fuzzy vector quantization, and the speaker adaptive recognition rate of the ESFVQ is increased by 26% than that without a speaker adaptation and by 11% than that of a vector quantization.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.348-354
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• 2003

This proposes a new algorithm for advanced personal identification system using iris pattern and speech signal. Since the proposed algorithm adopts a fusion scheme to take advantage of iris recognition and speaker identification, it shows robustness for noisy environments. For evaluating the performance of the proposed scheme, we compare it with the iris pattern recognition and speaker identification respectively. In the experiments, the proposed method showed more 56.7% improvements than the iris recognition method and more 10% improvements than the speaker identification method for high quality security level. Also, in noisy environments, the proposed method showed more 30% improvements than the iris recognition method and more 60% improvements than the speaker identification method for high quality security level.

• Journal of IKEEE
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• v.28 no.2
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• pp.168-173
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• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.481-483
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• 1989

This paper is concentrated on the recognition of Korean Digits. The speech signals of each of digits are fed into computer through the 18 bandpass filters, AD converter. Spectrum input data are analyzed and used. BASIC program language is used for recognition performance and the result of recognition is outputed to computer screen and printer. In this paper, the strength and weakness of filter-bank analysis method is described and the technique of real-time recognition is argued. In this experiment, Ratio of recognition for speaker dependent recognition was about 97% and recognition time was also satisfied. Therefore, A way of speaker independent recognition will be presented and using for special communication in the future.