Effects of a new triple-α\alpha reaction rate on the helium ignition of accreting white dwarfs

Effects of a new triple-alpha reaction rate on the ignition of carbon-oxygen white dwarfs accreting helium in a binary systems have been investigated. The ignition points determine the properties of a thermonuclear explosion of a Type Ia supernova. We examine the cases of different accretion rates of helium and different initial masses of the white dwarf, which was studied in detail by Nomoto. We find that for all cases from slow to intermediate accretion rates, nuclear burnings are ignited at the helium layers. As a consequence, carbon deflagration would be triggered for the lower accretion rate compared to that of $dM/dt\simeq 4\times10^{-8} M_{\odot} \rm yr^{-1}$ which has been believed to the lower limit of the accretion rate for the deflagration supernova. Furthermore, off-center helium detonation should result for intermediate and slow accretion rates and the region of carbon deflagration for slow accretion rate is disappeared.Comment: 4 pages, 2 figure

Sampling-based speech parameter generation using moment-matching networks

This paper presents sampling-based speech parameter generation using moment-matching networks for Deep Neural Network (DNN)-based speech synthesis. Although people never produce exactly the same speech even if we try to express the same linguistic and para-linguistic information, typical statistical speech synthesis produces completely the same speech, i.e., there is no inter-utterance variation in synthetic speech. To give synthetic speech natural inter-utterance variation, this paper builds DNN acoustic models that make it possible to randomly sample speech parameters. The DNNs are trained so that they make the moments of generated speech parameters close to those of natural speech parameters. Since the variation of speech parameters is compressed into a low-dimensional simple prior noise vector, our algorithm has lower computation cost than direct sampling of speech parameters. As the first step towards generating synthetic speech that has natural inter-utterance variation, this paper investigates whether or not the proposed sampling-based generation deteriorates synthetic speech quality. In evaluation, we compare speech quality of conventional maximum likelihood-based generation and proposed sampling-based generation. The result demonstrates the proposed generation causes no degradation in speech quality.Comment: Submitted to INTERSPEECH 201

Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities

Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.Comment: Accepted to INTERSPEECH 201

Computational Procedure for Estimating the Effects of Sea Sprays on Temperature Fields over the Sea Surface

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv