Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech.Comment: Accepted to ICASSP 201

An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process

Although Minimum Distance Parsing (MDP) offers a theoretically attractive solution to the problem of extragrammaticality, it is often computationally infeasible in large scale practical applications. In this paper we present an alternative approach where the labor is distributed between a more restrictive partial parser and a repair module. Though two stage approaches have grown in popularity in recent years because of their efficiency, they have done so at the cost of requiring hand coded repair heuristics. In contrast, our two stage approach does not require any hand coded knowledge sources dedicated to repair, thus making it possible to achieve a similar run time advantage over MDP without losing the quality of domain independence.Comment: 9 pages, 1 Postscript figure, uses aclap.sty and psfig.tex, In Proceedings of EMNLP 199

A High Quality Text-To-Speech System Composed of Multiple Neural Networks

While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing. We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images. The linguistic module employs a letter-to-sound neural network and a postlexical neural network. The acoustic module employs a duration neural network and a phonetic neural network. The visual neural network is employed in parallel to the acoustic module to drive a talking head. The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.Comment: Source link (9812006.tar.gz) contains: 1 PostScript file (4 pages) and 3 WAV audio files. If your system does not support Windows WAV files, try a tool like "sox" to translate the audio into a format of your choic

Some Requests for Machine Learning Research from the East African Tech Scene

Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.Comment: Presented at NIPS 2018 Workshop on Machine Learning for the Developing Worl

Foundation Funding for the Humanities: An Overview of Current and Historical Trends

Foundation Funding for the Humanities: An Overview of Current and Historical Trends, finds that funding for fields such as art history, history and archeology, languages and linguistics, area studies, and the humanistic social sciences increased two and one-half times (149.8 percent) from $134.1 million in 1992 to$335 million in 2002. At the same time the report notes that, despite the overall increase, some scholarly disciplines actually lost ground over the ten year period. Support for the humanities grew more slowly than overall foundation giving during this period (up 199.8 percent), and the share of giving for the humanities slipped from 2.5 percent in the early 1990s to 2.1 percent in 2002