Russian language

Summary

Russian (russkiy yazyk, ˈruskʲɪi̯ jɪˈzɨk) is an East Slavic language, spoken primarily in Russia. It is the native language of the Russians and belongs to the Indo-European language family. It is one of four living East Slavic languages, and is also a part of the larger Balto-Slavic languages. It was the de facto and de jure official language of the former Soviet Union. Russian has remained an official language in independent Russia, Belarus, Kazakhstan, and Kyrgyzstan, and is still commonly used as a lingua franca in Ukraine, the Caucasus, Central Asia, and to a lesser extent in the Baltic states and Israel. Russian has over 258 million total speakers worldwide. It is the most spoken Slavic language, and the most spoken native language in Europe, as well as the most geographically widespread language of Eurasia. It is the world's seventh-most spoken language by number of native speakers, and the world's eighth-most spoken language by total number of speakers. R

Official source

https://en.wikipedia.org/wiki/Russian_language

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Integrating Language Identification to improve Multilingual Speech Recognition

Holger Caesar

The process of determining the language of a speech utterance is called Language Identification (LID). This task can be very challenging as it has to take into account various language-specific aspects, such as phonetic, phonotactic, vocabulary and grammar-related cues. In multilingual speech recognition we try to find the most likely word sequence that corresponds to an utterance where the language is not known a priori. This is a considerably harder task compared to monolingual speech recognition and it is common to use LID to estimate the current language. In this project we present two general approaches for LID and describe how to integrate them into multilingual speech recognizers. The first approach uses hierarchical multilayer perceptrons to estimate language posterior probabilities given the acoustics in combination with hidden Markov models. The second approach evaluates the output of a multilingual speech recognizer to determine the spoken language. The research is applied to the MediaParl speech corpus that was recorded at the Parliament of the canton of Valais, where people switch from Swiss French to Swiss German or vice versa. Our experiments show that, on that particular data set, LID can be used to significantly improve the performance of multilingual speech recognizers. We will also point out that ASR dependent LID approaches yield the best performance due to higher-level cues and that our systems perform much worse on non-native data

Idiap2012

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In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four months of 2018. The proposed method has three steps. Firstly, it extracts the most trending articles over a chosen period of time. Secondly, it performs a semi-supervised topic extraction and thirdly, it compares topics across languages. The automated processing works with the data that combines Wikipedia's graph of hyperlinks, pageview statistics and summaries of the pages. The results show that people share a common interest and curiosity for entertainment, e.g. movies, music, sports independently of their language. Differences appear in topics related to local events or about cultural particularities. Interactive visualizations showing clusters of trending pages in each language edition are available online https://wiki-insights.epfl.ch/wikitrends

ACM2020

Cross-Platform Language Design in Scala.js (Keynote)

Sébastien Jean R Doeraene

Have you ever wondered what is the secret sauce of Scala.js? What defines Scala.js, above all else, is the overarching will to make it cross-platform. A cross-platform language is both portable-most source code cross-compiles and behaves the same way on multiple platforms-and interoperable-code written in that language can interoperate with other languages on the target platforms. Most multi-platform languages are designed with one of those two properties in mind, and only address the other one as an afterthought. This results in poor integration, difficulty to write portable code, or even the impossibility to use some platform-specific libraries. This talk shows how Scala.js was designed as a cross-platform language. We show how portability and interoperability drive every design decision, from which language feature to include to performance considerations. We state a criteria of completeness for interoperability, and show how Scala.js satisfies it (with the exception of one JavaScript language feature, still work-in-progress). We give insights about what features are critical for portability, and which ones can be relaxed in the name of performance. In particular, we stress the importance of the ecosystem of libraries: existing assumptions in the ecosystem can force the language designer into reproducing behaviors of Scala/JVM, even when they are accidental. As a practical exercise, we study the main language changes that made it into Scala.js 1.x wrt. 0.6.x-as well as possible future developments-and how they fit into the cross-platform language design methodology.

ASSOC COMPUTING MACHINERY2018