No abstract

Warm dense conditions in titanium foils irradiated with intense femtosecond laser pulses are diagnosed using an x-ray imaging spectroscopy technique. The line shapes of radially resolved titanium Kα spectra are measured with a toroidally bent GaAs crystal and an x-ray charge-coupled device. Measured spectra are compared with the K-shell emissions modeled using an atomic kinetics - spectroscopy simulation code. Kα line shapes are strongly affected by warm (5-40 eV) bulk electron temperatures and imply multiple temperature distributions in the targets. The spatial distribution of temperature is dependent on the target thickness, and a thin target shows an advantage to generate uniform warm dense conditions in a large area.

No abstract

In this paper, we propose a pronunciation variation modeling method for improving the performance of a non-native automatic speech recognition (ASR) system that does not degrade the performance of a native ASR system. The proposed method is based on an indirect data-driven approach, where pronunciation variability is investigated from the training speech data, and variant rules are subsequently derived and applied to compensate for variability in the ASR pronunciation dictionary. To this end, native utterances are first recognized by using a phoneme recognizer, and then the variant phoneme patterns of native speech are obtained by aligning the recognized and reference phonetic sequences. The reference sequences are transcribed by using each of canonical, knowledge-based, and hand-labeled methods. Similar to non-native speech, the variant phoneme patterns of non-native speech can also be obtained by recognizing non-native utterances and comparing the recognized phoneme sequences and reference phonetic transcriptions. Finally, variant rules are derived from native and non-native variant phoneme patterns using decision trees and applied to the adaptation of a dictionary for non-native and native ASR systems. In this paper, Korean spoken by Chinese native speakers is considered as the non-native speech. It is shown from non-native ASR experiments that an ASR system using the dictionary constructed by the proposed pronunciation variation modeling method can relatively reduce the average word error rate (WER) by 18.5% when compared to the baseline ASR system using a canonical transcribed dictionary. In addition, the WER of a native ASR system using the proposed dictionary is also relatively reduced by 1.1%, as compared to the baseline native ASR system with a canonical constructed dictionary.