RRepoGEO

REPOGEO REPORT · LITE

rtzr/Awesome-Korean-Speech-Recognition

Default branch main · commit bf08fcab · scanned 6/12/2026, 11:52:50 AM

GitHub: 530 stars · 36 forks

AI VISIBILITY SCORE
20 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface rtzr/Awesome-Korean-Speech-Recognition, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README introduction to clarify repo's nature

    Why:

    CURRENT
    A curated list of **Korean speech recognition** resources for developers, including **the error rate (Character Error Rate)** of Speech Recognition API on public datasets.
    COPY-PASTE FIX
    This repository is a **benchmark and comparison report** of various **Korean Speech Recognition (STT) APIs**, providing developers with a curated list of services and their measured **Character Error Rates (CER)** on public datasets. It does not offer an STT API itself, but rather evaluates existing ones.
  • mediumtopics#2
    Update topics to better reflect 'benchmark' and 'comparison'

    Why:

    CURRENT
    awesome, korean, speech-recognition, speech-to-text, speech-to-text-api, whisper
    COPY-PASTE FIX
    awesome-list, korean-speech-recognition, speech-to-text, stt-api-benchmark, api-comparison, character-error-rate, whisper
  • lowreadme#3
    Add a 'Who is this for?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Who is this for?' or 'Target Audience', with text such as: 'This repository is ideal for developers, researchers, and data scientists who need to evaluate and select the best Korean Speech-to-Text (STT) APIs for their projects, or understand the performance benchmarks of various services on Korean datasets.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface rtzr/Awesome-Korean-Speech-Recognition
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Speech-to-Text
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Speech-to-Text · recommended 2×
  2. AWS Amazon Transcribe · recommended 2×
  3. Naver Clova Speech (CSR) · recommended 1×
  4. Kakao AI (Kakao i) · recommended 1×
  5. Microsoft Azure Cognitive Services - Speech · recommended 1×
  • CATEGORY QUERY
    What are the best performing Korean speech-to-text APIs available for developers?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. Naver Clova Speech (CSR)
    3. Kakao AI (Kakao i)
    4. Microsoft Azure Cognitive Services - Speech
    5. AWS Amazon Transcribe
    6. Deepgram
    7. AssemblyAI

    AI recommended 7 alternatives but never named rtzr/Awesome-Korean-Speech-Recognition. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comparison of Korean speech recognition API accuracy and error rates?
    you: not recommended
    AI recommended (in order):
    1. Naver Clova Speech (Naver Cloud Platform)
    2. Kakao i (Kakao Enterprise)
    3. Google Cloud Speech-to-Text
    4. AWS Amazon Transcribe
    5. Microsoft Azure Cognitive Services Speech

    AI recommended 5 alternatives but never named rtzr/Awesome-Korean-Speech-Recognition. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of rtzr/Awesome-Korean-Speech-Recognition?
    pass
    AI did not name rtzr/Awesome-Korean-Speech-Recognition — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts rtzr/Awesome-Korean-Speech-Recognition in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name rtzr/Awesome-Korean-Speech-Recognition — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo rtzr/Awesome-Korean-Speech-Recognition solve, and who is the primary audience?
    pass
    AI did not name rtzr/Awesome-Korean-Speech-Recognition — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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rtzr/Awesome-Korean-Speech-Recognition — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite