OT but is any work anywhere being done with Japanese pronunciation problem?

Japanese language are often described as using multiple type of alphabets - kanji, kana, numbers, and English alphabets sometimes - and pronunciations of especially kanji is not very well constrained, creating tons of homophones and homographs, e.g. "koushou" shared across more than 20 words, and the character for "life" said to be involved in more than 150 differently read parts of words.

Even OT but Unicode code space used for Japanese Kanji is famously shared with Chinese Hanzi, leading to ambiguities.

This situation is causing AI-based TTS(and also image generators) trained directly on Unicode text to go weird on kanji, even for simple ones as "tomorrow". Classical pre-LLM Japanese TTS avoid this by operating on generated or manually specified pronunciations, skipping kanji altogether, which do occasionally lead to wrong readings, but won't lead to sound generation code creating butchered middle-of-road sounds.

It doesn't seem like most or any of AI TTS tackle this problem, but I'm not in that field. Do anyone know the statuses on it?

If you don’t mind sharing, how much does that cost you to integrate the translation API, and the text to speech API you’re using? Just curious as I’ve been thinking about doing something in that area (not anki or translations, but also language learning related).

Great project, and congrats for launching :)

I don't know what resolution or display you built this on, but a heads up the initial impression on my 4K monitor is that everything is incredibly tiny.

Very cool!

Are you willing to share more technical details?

- Which data sources do you ingest?

- How do you transform and enrich the data? How does your pipeline look?

- What are your key challenges?

- Which tools do you use? What is your 'stack'? (Stanze, wordfreq, Whisper, wn, ...)

Background: I am currently building a multi-lang vocabulary hub for language learning. The goal is to match core words/lemmas to their senses/concepts, and then be able to generate multi-language flash cards.

I am still stuck on the sense alignment and fingerprinting (example: should 'to shop', 'einkaufen', ' alışveriş yapmak' and 'go shopping' point to the same concept of 'shop'?), but in a later stage I want to allow user-submission and data enrichment for IPA, pictograms [1] and audio.

[1: https://arasaac.org/pictograms/search]

Use-case (the dream): I come back from language class, I input new vocab and I output new Anki cards that work across all my fluent languages.

Currently, I mostly find myself knee-deep in problems of linguistics, NLP, Python and getting an LLM to do exactly what I want. At the same time it is a super fun project, and really makes me feel the joy of programming again. LLMs are magic, time just flies by, and all the random projects I always wanted to do suddenly materialize.

For coding, I mostly use free Gemini and some deepseek-v4-flash via openrouter to keep a tight oversight and understand the problem space. Maybe this slows me down, but agentic code jsut does not align with me. Overall, I haven't spent more than 2 € in total.

So far, surprisingly, the biggest problem is the lack of high-quality, free input data (example: English has the Oxford 5000 words as core vocabulary, but it is difficult to find the same for e.g. Turkish).

2nd place is the lack of high-quality synsets/wordnets (cross-language is mostly incomplete), and the 3rd place is getting LLMs to reliable play to their strength (on paper, a LLM is the perfect tool to provide multi-lang sense equivalents)

I plan to do a full writeup sometimes, but first I need it to work :)

I like the structure of their privacy policy page [0] and how it appears that they are not data-greedy.

And the site itself is a great idea and implementation, though the font size and family of the ui (not of the actual playback area) has a lot of room for improvement, but those are just minor changes.

[0] https://lingochunk.com/privacy

Is it possible to add traditional characters for mandarin?

Also the pinyin for 誰/谁 is coming through as shuí, whilst this character has two pronounciations, I believe shéi is the more common one.

This is awesome! I’ll be lurking for new data sources. I’m working on a self-hosted language app more focused around cloze and sentence mining into Anki. I love seeing more stuff happening in this space

I also built a tool to help me study Spanish. I really like the idea of shadowing, so I built a tool that lets you take any YouTube video and generate a sentence-by-sentence exercise to help you repeat the speaker's phrases.

https://talkhabit.com/shadow Or example, of one exercise: https://talkhabit.com/shadow?videoUrl=https%3A%2F%2Fwww.yout...

Stuff I need to work on: - It only works with videos that have auto-generated captions - It works best with monologue videos

What are you doing for Chinese word segmentation/pinyin?

Just tried it with an unsupported language and it still worked I set it to Chinese and inputted the audio. Still got correct results.

Very cool! I'm also learning Greek and it's amazing how many resources are becoming available.

Very nice work. I'm going for a different thing, but my audio2anki tool [1] is about as streamlined as I could make it to turn a YouTube URL I want to learn into a stack of Anki flashcards, purely locally.

[1]: https://github.com/hiAndrewQuinn/audio2anki

This is really cool, just as I'm starting to get towards the back end of the Kaishi 1.5k deck so this will be perfect for my Japanese studies. Thanks for sharing.