RRepoGEO

REPOGEO REPORT · LITE

edobashira/speech-language-processing

Default branch master · commit d1d815f2 · scanned 5/16/2026, 1:52:52 AM

GitHub: 2,225 stars · 291 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 edobashira/speech-language-processing, 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 H1 to clearly state it's a curated list

    Why:

    CURRENT
    Speech and Natural Language Processing
    COPY-PASTE FIX
    # Awesome Speech and Natural Language Processing Resources
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, speech-processing, natural-language-processing, nlp, speech-recognition, language-modeling, computational-linguistics, resources
  • mediumlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file, for example, using the CC-BY-4.0 license, which is common for content-based repositories like curated lists.

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 edobashira/speech-language-processing
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. allenai/allennlp · recommended 1×
  3. explosion/spaCy · recommended 1×
  4. nltk/nltk · recommended 1×
  5. speechbrain/speechbrain · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of resources for speech and natural language processing?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Datasets (huggingface/transformers)
    2. AllenNLP (allenai/allennlp)
    3. spaCy (explosion/spaCy)
    4. NLTK (nltk/nltk)
    5. SpeechBrain (speechbrain/speechbrain)
    6. Kaldi (kaldi-asr/kaldi)

    AI recommended 6 alternatives but never named edobashira/speech-language-processing. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source finite-state transducer libraries for text processing tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenFST (openfst/openfst)
    2. Foma (mhulden/foma)
    3. HFST (hfst/hfst)
    4. PyFST (pyfst/pyfst)
    5. AT&T FSM Library (att/fsm)

    AI recommended 5 alternatives but never named edobashira/speech-language-processing. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    Suggestion:

  • 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 edobashira/speech-language-processing?
    pass
    AI named edobashira/speech-language-processing explicitly

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

  • If a team adopts edobashira/speech-language-processing in production, what risks or prerequisites should they evaluate first?
    pass
    AI named edobashira/speech-language-processing explicitly

    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 edobashira/speech-language-processing solve, and who is the primary audience?
    pass
    AI did not name edobashira/speech-language-processing — 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?

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edobashira/speech-language-processing — 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