• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.447-452
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• 2016

In this study, we fabricated and evaluated the performance of film speaker using PVDF/ZnO NP composite structure. PVDF piezoelectric films were fabricated and characterized by XRD and SEM. ZnO nanopillars were prepared by hydrothermal synthesis on prepared PVDF piezoelectric films. We analyzed and tested the acoustic signal characteristics of the piezoelectric film. In order to fabricate an acoustic structure with a wide frequency range from low to high frequency, we have fabricated various types of film speakers and investigated the frequency characteristics. As a result, the fundamental piezoelectric properties of PVDF show that the piezoelectric constant due to ZnO NP increases. And the overall acoustic signal level is also increased by 10% or more. We investigated frequency generation from 500 Hz to 10 KHz using different sizes with PVDF/ZnO NP composite film speaker.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2785-2799
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• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

• Speech Sciences
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• v.12 no.3
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• pp.105-114
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• 2005

It was verified that a speaker verification system improved its performances of EER by regularizing log likelihood ratio, using background speaker models. Recently the wireless mobile phones are becoming more dominant communication terminals than wired phones. So the need for building a speaker verification system on mobile phone is increasing abruptly. Therefore in this paper, we had some experiments to examine the performance of speaker verification based on mobile phone's voices. Especially we are focused on the performance variations in EER(Equal Error Rate) according to several background speaker's characteristics, such as selecting methods(MSC, MIX), number of background speakers, aging factor of speech database. For this, we constructed a speaker verification system that uses GMM(Gaussin Mixture Model) and found that the MIX method is generally superior to another method by about 1.0% EER. In aspect of number of background speakers, EER is decreasing in proportion to the background speakers populations. As the number is increasing as 6, 10 and 16, the EERs are recorded as 13.0%, 12.2%, and 11.6%. An unexpected results are happened in aging effects of the speech database on the performance. EERs are measured as 4%, 12% and 19% for each seasonally recorded databases from session 1 to session 3, respectively, where duration gap between sessions is set by 3 months. Although seasons speech database has 10 speakers and 10 sentences per each, which gives less statistical confidence to results, we confirmed that enrolled speaker models in speaker verification system should be regularly updated using the ongoing claimant's utterances.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.909-917
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• 2007

Recently the trend of automobile industry is that IQS evaluation index against a sensitivity quality is increasing. To reduce rattle noise due to speaker sound at low frequencies, it is required the advanced investigation of a package tray panel and a door module panel. This paper optimized the design parameters of package tray panel according to the theoretical background about robust design and suggested the design guideline for resonance avoidance and the reduction of vibrational sensitivity considering the excitation frequency of woofer speaker. In addition, it is suggested the design guideline of a door module panel through the sensitivity analysis in case of the speaker excitation. Finally, the design factor analysis of the quality deviation of a mother-car will make it possible to guarantee the stable characteristics of vehicle vibration in the early stage of vehicle development. These improvements can lead to shortening the time needed to develop better vehicles.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.8
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• pp.673-682
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• 2007

Recently the trend of automobile industry is that IQS evaluation index against a sensitivity quality is increasing. To reduce rattle noise due to speaker sound at low frequencies, it is required the advanced technology analysis process of body structure. This paper optimized the design parameters of package tray panel according to the theoretical background about robust design and suggested the design guideline for resonance avoidance and the reduction of vibrational sensitivity considering the excitation frequency of woofer speaker. And this paper described the design process of a door module panel through the sensitivity analysis in case of the door speaker excitation. Finally, the analysis of the quality deviation using mother car is suggested to guarantee the stable characteristics of vehicle vibration in the early stage of vehicle development. These improvements can lead to shortening the time needed to develop better vehicles.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.403-407
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• 2003

Recently, eigenvoice approach has been widely used for rapid speaker adaptation. However, even in the eigenvoice approach, Performance improvement using very small amount of adaptation data is relatively small in comparison with that using somewhat large adaptation data because the reliable estimation of weights of eigenvoice is difficult. In this paper, we propose a rapid speaker adaptation method based on eigenvoice using the weight distribution characteristics to improve the performance on a small adaptation data. In the Experimental results on vocabulary-independent word recognition task (using PBW 452 database), the weight threshold method alleviates the problem of relatively low performance for a tiny small adaptation data. When single adaptation word is used, word error rate is reduced about 9-18% by the weight threshold method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8140-8146
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• 2015

The objective of this study is to experimentally investigate the cooling performance characteristics with the consideration of the temperature variations of the enclosure of the 400W ferrofluid active speaker having both woofer and amplifier heat sinks. In order to do this, the heat sinks for both woofer and amplifier was designed ant applied to 400W ferrofluid active speaker. As a result, the cooling performance of the developed 400W ferrofluid active speaker was improved and the temperature of the enclosure after 120 min at steady state increased by $2.8^{\circ}C$ with the increase of the outdoor temperatures from $25^{\circ}C$ to $29^{\circ}C$. In addition, the overall sound pressure level of the developed 400W ferrofluid active speaker showed 111.8 dB and improved 1.9 dB higher than 109.9 dB of the existed speaker.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.138-154
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• 2019

This study was conducted to identify the use motivations of AI speaker and examine the characteristics of AI speaker users. Based on the uses and gratifications theory, The study results show that the user motivations of AI speaker are four dimensional, namely escaping from daily problems and maintaining social relationships, information acquisition and learning, entertainment and relaxation and pursuit of practicability. The main AI speaker users are in their 30s, and they are innovative to actively use AI speakers for entertainment purposes such as listening to music. The four sub-dimensions differed as we compared them with user characteristics. Specifically, the motivation for escaping from daily problems and maintaining social relationships varied with gender and age. Moreover, age and informativeness were identified to have an influence on the motivations of information acquisition and learning and entertainment and relaxation. In sum, this research provides practical implications into how to strategically create contents and services for AI speakers.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1E
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• pp.16-20
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• 2005

A novel scheme to measure the speaker information in speech signal is proposed. We develope the theory of quantitative measurement of the speaker characteristics in the information theoretic point of view, and connect it to the classification error rate. Homomorphic analysis based features, such as mel frequency cepstral coefficient (MFCC), linear prediction cepstral coefficient (LPCC), and linear frequency cepstral coefficient (LFCC) are studied to measure speaker specific information contained in those feature sets by computing mutual information. Theories and experimental results provide us quantitative measure of speaker information in speech signal.