In this paper, we present a solution for providing personalized and instant
feedback to English learners through a mobile application, called Flowchase,
that is connected to a speech technology able to segment and analyze speech
segmental and supra-segmental features. The speech processing pipeline receives
linguistic information corresponding to an utterance to analyze along with a
speech sample. After validation of the speech sample, a joint forced-alignment
and phonetic recognition is performed thanks to a combination of machine
learning models based on speech representation learning that provides necessary
information for designing a feedback on a series of segmental and
supra-segmental pronunciation aspects.

本文提出一种通过连接语音技术以提供个性化及即时反馈给英语学习者的移动应用 Flowchase，该应用程序能分析语音部分和超音段特征的技术解决方案，并使用基于语音表示学习的机器学习模型进行验证、联合强制排列以及音素识别，从而设计一系列声准和声位方面的反馈。

Flowchase：一个用于发音练习的移动应用

Flowchase: a Mobile Application for Pronunciation Training

Enhancing word usage is a desired feature for writing assistance. To further
advance research in this area, this paper introduces "Smart Word Suggestions"
(SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end
evaluation and presents a more realistic writing assistance scenario. This task
involves identifying words or phrases that require improvement and providing
substitution suggestions. The benchmark includes human-labeled data for
testing, a large distantly supervised dataset for training, and the framework
for evaluation. The test data includes 1,000 sentences written by English
learners, accompanied by over 16,000 substitution suggestions annotated by 10
native speakers. The training dataset comprises over 3.7 million sentences and
12.7 million suggestions generated through rules. Our experiments with seven
baselines demonstrate that SWS is a challenging task. Based on experimental
analysis, we suggest potential directions for future research on SWS. The
dataset and related codes is available at
this https URL

本篇研究介绍了 “Smart Word Suggestions”（SWS）任务和基准，该任务强调端到端评估，旨在提高写作辅助的效果。SWS 需要识别需要改进的单词或短语，并提供替换建议，实验结果表明 SWS 是一项富有挑战性的任务，为未来的研究提供了方向。