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NetProf iOS pronunciation feedback demonstration

Published in:
IEEE Automatic Speech Recognition and Understanding Workshop, ASRU, 13 December 2015.

Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are not widely studied in the U.S. Many students struggle to attain speaking fluency and proper pronunciation. Teaching pronunciation is a time-intensive task for teachers that requires them to give individual feedback to students during classroom hours. This limits the time teachers can spend imparting other information, and students may feel embarrassed or inhibited when they practice with their classmates. Given the demand for students educated in foreign languages and the limited number of qualified teachers in languages of interest, there is a growing need for computer-based tools students can use to practice and receive feedback at their own pace and schedule. Most existing tools are limited to listening to pre-recorded audio with limited or nonexistent support for pronunciation feedback. MIT Lincoln Laboratory has developed a new tool, Net Pronunciation Feedback (NetProF), to address these challenges and improve student pronunciation and general language fluency.
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Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are...

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A new multiple choice comprehension test for MT

Published in:
Automatic and Manual Metrics for Operation Translation Evaluation Workshop, 9th Int. Conf. on Language Resources and Evaluation (LREC 2014), 26 May 2014.

Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic documents into English from two different MT systems. We created two forms of the test: Form A has the original English documents and output from the two Arabic-to-English MT systems. Form B has English, Arabic, and one of the MT system outputs. We administered the comprehension test to three subject types recruited in the greater Boston area: (1) native English speakers with no Arabic skills, (2) Arabic language learners, and (3) Native Arabic speakers who also have English language skills. There were 36 native English speakers, 13 Arabic learners, and 11 native Arabic speakers with English skills. Subjects needed an average of 3.8 hours to complete the test, which had 191 questions and 59 documents. Native English speakers with no Arabic skills saw Form A. Arabic learners and native Arabic speakers saw form B.
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Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic...

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Standardized ILR-based and task-based speech-to-speech MT evaluation

Published in:
Automatic and Manual Metrics for Operation Translation Evaluation Workshop, 9th Int. Conf. on Language Resources and Evaluation (LREC 2014), 26 May 2014.

Summary

This paper describes a new method for task-based speech-to-speech machine translation evaluation, in which tasks are defined and assessed according to independent published standards, both for the military tasks performed and for the foreign language skill levels used. We analyze task success rates and automatic MT evaluation scores (BLEU and METEOR) for 220 role-play dialogs. Each role-play team consisted of one native English-speaking soldier role player, one native Pashto-speaking local national role player, and one Pashto/English interpreter. The overall PASS score, averaged over all of the MT dialogs, was 44%. The average PASS rate for HT was 95%, which is important because a PASS requires that the role-players know the tasks. Without a high PASS rate in the HT condition, we could not be sure that the MT condition was not being unfairly penalized. We learned that success rates depended as much on task simplicity as it did upon the translation condition: 67% of simple, base-case scenarios were successfully completed using MT, whereas only 35% of contrasting scenarios with even minor obstacles received passing scores. We observed that MT had the greatest chance of success when the task was simple and the language complexity needs were low.
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Summary

This paper describes a new method for task-based speech-to-speech machine translation evaluation, in which tasks are defined and assessed according to independent published standards, both for the military tasks performed and for the foreign language skill levels used. We analyze task success rates and automatic MT evaluation scores (BLEU and...

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A language-independent approach to automatic text difficulty assessment for second-language learners

Published in:
Proc. 2nd Workshop on Predicting and Improving Text Readability for Target Reader Populations, 4-9 August 2013.

Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words for Arabic, Dari, English, and Pashto. We compare Support Vector Machines and the Margin-Infused Relaxed Algorithm measured by mean squared error. We provide an analysis of which features are most predictive of a given level.
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Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words...

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