http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Nam Soo Kim,June Sig Sung,Doo Hwa Hong IEEE 2011 IEEE signal processing letters Vol.18 No.2
<P>One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this letter, we extend MLLR to factored MLLR (FMLLR) in which the MLLR parameters depend on a continuous-valued control vector. Since it is practically impossible to estimate the MLLR parameters for each control vector separately, we propose a compact parametric form of the MLLR parameters. In the proposed approach, each MLLR parameter is represented as an inner product between a regression vector and transformed control vector. We present an algorithm to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. The proposed approach is applied to adapt the HMM parameters obtained from a database of reading-style speech to singing-style voices while treating the pitches and durations extracted from the musical notes as the control vectors. This enables to efficiently construct a singing voice synthesizer with only a small amount of singing data.</P>
June Sig Sung,Doo Hwa Hong,Nam Soo Kim IEEE 2014 IEEE journal of selected topics in signal processi Vol.8 No.2
<P>Speech synthesized from the same text should sound differently depending on the speaking style. Current speech synthesis techniques based on the hidden Markov model (HMM) usually focus on a fixed speaking style and changing the speaking style requires a variety of sets of parameters trained in different speaking styles. A promising alternative is to adapt the base model to the intended speaking style. In our previous work, we proposed factored maximum likelihood linear regression (FMLLR) adaptation where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. In this paper, we introduce a novel technique called factored maximum penalized likelihood kernel regression (FMLKR) for HMM-based style adaptive speech synthesis. In FMLKR, nonlinear regression between the mean vector of the base model and the corresponding mean vectors of the adaptation data is performed with the use of kernel method based on the FMLLR framework. In a series of experiments on artificial generation of singing voice and expressive speech, we evaluate the performance of the FMLLR and FMLKR techniques with various matrix structures and also compare with other approaches to parameter adaptation in HMM-based speech synthesis.</P>
Photonic Bandgap Structures with Arrays of Spiral metal Patches
Jho, Won-June,Yeom, Dong-Hyuk,Yoon, Chang-Joon,Cho, Kyoung-Ah,Kim, Sang-Sig Institute of Korean Electrical and Electronics Eng 2007 전기전자학회논문지 Vol.11 No.4
A new type of photonic bandgap(PBG) structures that consist of arrays of spiral metal patches is proposed in this paper. Reflection phases and radiation of these PBG structures are simulated by high frequency structure simulator(HFSS) to characterize their performance. The simulation results show that the resonant frequency of the proposed PBG structures gets significantly lower than those of the PBG structures with square metal patches, but that the radiation is nearly the same for both of the PBG structures. Analysis on reflection phases reveals that the lowering of the resonant frequency is associated with the increase in capacitance.