BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition

Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech recognition. There are two key features in BridgeNet. First, BridgeNet extends traditional student-teacher frameworks by providing multiple hints from a teacher network. Hints are not limited to the soft labels from a teacher network. Teacher's intermediate feature representations can better guide a student network to learn how to denoise or dereverberate noisy input. Second, the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance. The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem, where it achieved up to 13.24% relative WER reductions on AMI corpus compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Highly tunable repetition-rate multiplication of mode-locked lasers using all-fibre harmonic injection locking

Higher repetition-rate optical pulse trains have been desired for various applications such as high-bit-rate optical communication, photonic analogue-to-digital conversion, and multi- photon imaging. Generation of multi GHz and higher repetition-rate optical pulse trains directly from mode-locked oscillators is often challenging. As an alternative, harmonic injection locking can be applied for extra-cavity repetition-rate multiplication (RRM). Here we have investigated the operation conditions and achievable performances of all-fibre, highly tunable harmonic injection locking-based pulse RRM. We show that, with slight tuning of slave laser length, highly tunable RRM is possible from a multiplication factor of 2 to >100. The resulting maximum SMSR is 41 dB when multiplied by a factor of two. We further characterize the noise properties of the multiplied signal in terms of phase noise and relative intensity noise. The resulting absolute rms timing jitter of the multiplied signal is in the range of 20 fs to 60 fs (10 kHz - 1 MHz) for different multiplication factors. With its high tunability, simple and robust all-fibre implementation, and low excess noise, the demonstrated RRM system may find diverse applications in microwave photonics, optical communications, photonic analogue-to-digital conversion, and clock distribution networks.Comment: 25 pages, 9 figure

What data characteristics are needed for data reuse in the domain of social sciences in Korea?

With the benefits of data sharing and reuse, data reuse have been promoted in various domains. While there are practices and discussions regarding data sharing and reuse, we still have little knowledge on what characteristics of data impact decisions on data reuse. In this sense, we aim to explore data characteristics in the context of data reuse within the domain of social sciences in Korea. For the purpose of this study, we conducted in-depth interviews with twelve re-searchers in the field of social science in terms of six dimensions: data producer, country/language, data type/collection method, procedure, accessibility, size/currency. For the producer dimension, social scientists preferred data that have been produced by an institution rather than an individual researcher. In language used in the data sets, English were more favored because researchers preferred English than any other languages. In terms of data type, quantitative and survey data types are preferred. For the procedure of data, researchers preferred original raw data with plenty of metadata and demographic information for analysis. For accessibility, there was less preference for restricted data. Lastly, for size/currency, researchers showed a preference for big size data and current data. These preliminary findings can provide better understanding about data reuse and guide improved data reuse services