REPOGEO REPORT · LITE
speechio/chinese_text_normalization
Default branch master · commit 5d81cefc · scanned 6/15/2026, 8:18:04 AM
GitHub: 732 stars · 151 forks
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 speechio/chinese_text_normalization, 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
2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- mediumtopics#1Add more specific topics to improve categorization
Why:
CURRENTasr, chinese, kaldi-asr, sparrowhawk, speech-recognition, text-normalization, thrax-gramma
COPY-PASTE FIXasr, chinese, kaldi-asr, sparrowhawk, speech-recognition, text-normalization, thrax-gramma, mandarin, tts, speech-synthesis, nlp-for-speech
- lowreadme#2Complete the 'Normalizers' section in the README
Why:
CURRENT## Normalizers 1. support
COPY-PASTE FIX## Normalizers 1. **Numbers:** Convert numerical expressions (e.g., dates, times, currency, fractions) into their spoken forms. 2. **Punctuation:** Handle various punctuation marks for appropriate speech pauses and intonation. 3. **Abbreviations:** Expand common abbreviations into full words. 4. **Symbols:** Normalize symbols and special characters relevant to Chinese text. 5. **Custom Rules:** Support for user-defined rules to handle specific domain-dependent normalizations.
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.
- OpenNMT-py · recommended 1×
- Fairseq · recommended 1×
- Pynini · recommended 1×
- HanLP · recommended 1×
- Jieba · recommended 1×
- CATEGORY QUERYHow to normalize Chinese text for speech recognition applications?you: not recommendedAI recommended (in order):
- OpenNMT-py
- Fairseq
- Pynini
- HanLP
- Jieba
- Stanford CoreNLP
AI recommended 6 alternatives but never named speechio/chinese_text_normalization. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the best open-source tools for Mandarin text normalization in ASR?you: not recommendedAI recommended (in order):
- Thunlp/THULAC (Thunlp/THULAC)
- PaddleNLP
- OpenNMT-py (OpenNMT/OpenNMT-py)
- Stanford NLP
- Jieba (fxsjy/jieba)
AI recommended 5 alternatives but never named speechio/chinese_text_normalization. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 speechio/chinese_text_normalization?passAI did not name speechio/chinese_text_normalization — 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 speechio/chinese_text_normalization in production, what risks or prerequisites should they evaluate first?passAI named speechio/chinese_text_normalization 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 speechio/chinese_text_normalization solve, and who is the primary audience?passAI did not name speechio/chinese_text_normalization — 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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speechio/chinese_text_normalization — 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