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In this paper we present a context-aware mobile language learning application, focused on providing language support to users living in foreign countries.
The FLOM model has been evaluated on an image-based language learning application, which has been designed for interpretation of Japanese kanji and Mayan glyphs, using emerging data exchange technologies and some approaches for retrieval and visualization of multimedia documents in mobile environments.
This is encouraging, especially if we keep in mind that in a language learning application we can be conservative, in the sense that if we are not sufficiently confident about the recognition result we can always ask the language learner to try again.
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Initially, the company's focus was on building conversational-based language learning applications for kids.
Since the non-native component of the JASMIN corpus was collected for the aim of facilitating the development of ASR-based language learning applications, it is particularly suited for our purpose.
Computer-Assisted Language Learning (CALL) applications for improving the oral skills of low-proficient learners have to cope with non-native speech that is particularly challenging.
One of the application domains of u-learning or CSUL is language learning.
Solid academic toys include math and science kits, as well as language learning toys, and these toys should actively engage children in skill acquisition or application.
The increasing demand for innovative applications that support language learning has led to a growing interest in Computer-Assisted Language Learning (CALL) systems that make use of ASR technology.
Content based language learning.
Call for more language learning.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com