Towards Language-Universal End-to-End Speech Recognition

Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters. In this work, we exploit recent progress in end-to-end speech recognition to create a single multilingual speech recognition system capable of recognizing any of the languages seen in training. To do so, we propose the use of a universal character set that is shared among all languages. We also create a language-specific gating mechanism within the network that can modulate the network's internal representations in a language-specific way. We evaluate our proposed approach on the Microsoft Cortana task across three languages and show that our system outperforms both the individual monolingual systems and systems built with a multi-task learning approach. We also show that this model can be used to initialize a monolingual speech recognizer, and can be used to create a bilingual model for use in code-switching scenarios.Comment: submitted to ICASSP 201

The Lixiscope: a Pocket-size X-ray Imaging System

A Low Intensity X ray Imaging device with the acronym LIXISCOPE is described. The Lixiscope has a small format and is powered only by a 2.7V battery. The high inherent gain of the Lixiscope permits the use of radioactive sources in lieu of X-ray machines in some fluoroscopic applications. In this mode of operation the complete X ray imaging system is truly portable and pocket-sized

Improved training for online end-to-end speech recognition systems

Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training. Otherwise, the networks may fail to find a good local optimum. This is particularly true for online networks, such as unidirectional LSTMs. Currently, the best strategy to train such systems is to bootstrap the training from a tied-triphone system. However, this is time consuming, and more importantly, is impossible for languages without a high-quality pronunciation lexicon. In this work, we propose an initialization strategy that uses teacher-student learning to transfer knowledge from a large, well-trained, offline end-to-end speech recognition model to an online end-to-end model, eliminating the need for a lexicon or any other linguistic resources. We also explore curriculum learning and label smoothing and show how they can be combined with the proposed teacher-student learning for further improvements. We evaluate our methods on a Microsoft Cortana personal assistant task and show that the proposed method results in a 19 % relative improvement in word error rate compared to a randomly-initialized baseline system.Comment: Interspeech 201

Cost effective development of a national test bed

For several years, the Marshall Space Flight Center has pursued the coordinated development of a Large Space Structures (LSS) National Test Bed for the investigation of numerous technical issues involved in the use of LSS in space. The origins of this development, the current status of the various test facilities and the plans laid down for the next five years' activities are described. Particular emphasis on the control and structural interaction issues has been paid so far; however, immediately emerging are user applications (such as the proposed pinhole occulter facility). In the immediate future, such emerging technologies as smart robots and multibody interactions will be studied. These areas are covered

Preparation for meaningful work and life: urban high school youth's reflections on work-based learning 1 year post-graduation

The challenges confronted by low-income high school students throughout school and across the transition to higher education and employment are well-documented in the US and many other nations. Adopting a positive youth development perspective (Lerner et al., 2005), this study reports findings from interviews with 18 low-income, racially and ethnically diverse graduates of an urban Catholic high school in the US. The interviews were designed to shed light on the post-high school experiences of urban high school graduates and to understand how students construct meaning about the value of school and work-based learning (WBL) in their preparation for meaningful work and life. The interviews highlight the perceived value of the academic and non-cognitive preparation students experienced through high school and WBL in relation to the challenges they encountered along the pathway to post-high school success and decent work. Overall, the findings suggest the potential of WBL for low-income youth in facilitating access to resources that build academic and psychological/non-cognitive assets, while also illustrating the role of structural and contextual factors in shaping post-high school transitions and access to meaningful work and life opportunities.Published versio

Indiana Nonprofits: Scope and Community Dimensions

This report presents new data on the size, composition, and distribution of paid employment over the 1995-2011 time period in Indiana's private nonprofit organizations in a broad range of industries traditionally dominated by for-profit industries. Nonprofit organizations make significant contributions to the quality of life for the residents of Indiana and are a major force in the state's economy. This is particularly the case for the industries where nonprofits play a major role, such as health care, social assistance, education, arts, culture and recreation, and membership associations. However, very little is known about the large number of nonprofits that are scattered across virtually all other industries in Indiana where for-profit establishments dominate. This report provides an overview of nonprofit employment in all the other "minor" nonprofit industries