Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Effect of a Web-enhanced Clinical Practicum on Learning Outcome of Adult Nursing Practicum in Nursing Students

Abstract: This study was conducted to develop Web-based multimedia content that assists undergraduate students in a clinical practicum on adult nursing. The study examined whether students in the intervention group could obtain clinical knowledge and perform more effectively when encouraged to learn Web content as compared with students in the conventional group. Web-based multimedia content consisting of 13 learning modules was developed based on real patients’ scenarios through collaboration among college professors. A total of 120 nursing students (74 for the intervention and 46 for the conventional groups) from two universities in Gcity, who engaged in a 3-week long clinical practicum in the digestive and respiratory units of a university hospital, participated in the study. Students’ knowledge, self-directed learning, and clinical performance ability were measured using self-administered questionnaires. Data for pre- and posttests were collected over a 2-month period, between May and June of 2009. Clinical knowledge and self-reported clinical performance scores were significantly higher in students using the Web-enhanced clinical practicum than in those in the conventional group. However, there was no significant difference in self-directed learning ability between the 2 groups. These results demonstrate that Web-based multimedia content can be an effective educational tool for enhancing students’ clinical knowledge and performance.

Improving LPCNET-Based Text-to-Speech with Linear Prediction-Structured Mixture Density Network

Abstract: In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining a vocal tract LP filter with a WaveRNN-based vocal source (i.e., excitation) generator. However, the quality of synthesized speech is often unstable because the vocal source component is insufficiently represented by the µ-law quantization method, and the model is trained without considering the entire speech production mechanism. To address this problem, we first introduce LP-MDN, which enables the autoregressive neural vocoder to structurally represent the interactions between the vocal tract and vocal source components. Then, we propose to incorporate the LP-MDN to the LPCNet vocoder by replacing the conventional discretized output with continuous density distribution. The experimental results verify that the proposed system provides high quality synthetic speech by achieving a mean opinion score of 4.41 within a text-to-speech framework.

Adapting Speaker Embeddings for Speaker Diarisation

Abstract: The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker diarisation systems. Despite this, prior works in the field have directly used embeddings designed only to be effective on the speaker verification task. In this paper, we propose three techniques that can be used to better adapt the speaker embeddings for diarisation: dimensionality reduction, attention-based embedding aggregation, and non-speech clustering. A wide range of experiments is performed on various challenging datasets. The results demonstrate that all three techniques contribute positively to the performance of the diarisation system achieving an average relative improvement of 25.07% in terms of diarisation error rate over the baseline.

LiteTTS: A Lightweight Mel-Spectrogram-Free Text-to-Wave Synthesizer Based on Generative Adversarial Networks

Abstract: In this paper, we propose a lightweight end-to-end text-tospeech model that can generate high-quality speech at breakneck speed. In our proposed model, a feature prediction module and a waveform generation module are combined within a single framework. The feature prediction module, which consists of two independent sub-modules, estimates latent space embeddings for input text and prosodic information, and the waveform generation module generates speech waveforms by conditioning on the estimated latent space embeddings. Unlike conventional approaches that estimate prosodic information using a pre-trained model, our model jointly trains the prosodic embedding network with the speech waveform generation task using an effective domain transfer technique. Experimental results show that our proposed model can generate samples 7 times faster than real-time, and about 1.6 times faster than FastSpeech 2, as we use only 13.4 million parameters. We confirm that the generated speech quality is still of a high standard as evaluated by mean opinion scores.

Autoignition Tests by Injecting Kerosene into Vortex of Decomposed Hydrogen Peroxide

Abstract: Using decomposed hydrogen peroxide as an oxidizer and kerosene as a fuel, a 1200 N vacuum thrust-class staged-bipropellant engine has been developed and tested with aim to investigate an axial fuel injector integrated a distributor. This fuel injector geometry that injects a fuel into turbulent flow of decomposed hydrogen peroxide was tested to evaluate influence of the designed injector on engine performance. For the characteristics such as autoignition and stable combustion, firing tests over a wide range of equivalence ratio from 0.26 to 1.61 were carried out. Autoignition was achieved in all experimental conditions and pressure variation in the combustion chamber was as low as ± 1%. The efficiency of characteristic velocity, C * , was measured at or over 100% in fuel-lean conditions and from 88% to 94% in fuel-rich conditions at the L * of 0.95 m.