About
Improve your Mandarin pronunciation with this app. It offers detailed comparisons of commonly confused sounds and words, AI-powered practice tests, and a vast library of phrases and reading materials. Track progress and focus on common errors.
What's New in 南方人怎么说一口标准普通话
4.2
May 4, 2021
修补了词条滑动的毛病。
In-App Purchases
$2.99
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Developer apps
FAQ
How does the pronunciation training work in the app?
The app focuses on common pronunciation errors for Mandarin speakers, especially those from Southern China. It provides detailed comparisons of easily confused sounds and words, along with AI-powered tests that adapt to your mistakes.
Can teachers or parents monitor progress?
Yes, teachers and parents can access online reports detailing student progress, including recent Pinyin scores and frequently mispronounced characters, allowing for targeted tutoring.
Does the app offer practice with full phrases and sentences?
Absolutely. Beyond single characters, the app includes over 70,000 common phrases and word groups to help users practice pronunciation in context.
What kind of reading materials are available?
The app features a selection of reading materials, including 800 children's songs, 150 Tang Dynasty poems, and 30 pieces each of 'Sheng Lü Qǐ Méng' and 'Lì Wēng Duì Yùn'.
How does the AI exam training function?
The AI generates personalized Pinyin exam questions, focusing on commonly confused sounds. When you make a mistake, it shows similar-sounding characters for direct comparison and adds incorrect words to your 'My Collection' for review.
Is the app suitable for beginners learning Mandarin?
While designed to correct specific pronunciation issues, the app's focus on fundamental Pinyin and common phrases makes it beneficial for learners aiming for clear standard Mandarin.
What is the update frequency of the app?
The app was last updated on May 4, 2021, indicating a history of development and potential for future improvements.
What is the user sentiment based on the rating?
The app has a rating of 1.0 stars from 1 rating, suggesting a very low user satisfaction level based on the available data.
