RRepoGEO

REPOGEO REPORT · LITE

Picovoice/speech-to-text-benchmark

Default branch master · commit 43e7689f · scanned 6/9/2026, 10:38:03 PM

GitHub: 693 stars · 73 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /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
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 Picovoice/speech-to-text-benchmark, 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 opening to clarify its role as a benchmark framework

    Why:

    CURRENT
    This repo is a minimalist and extensible framework for benchmarking different speech-to-text engines.
    COPY-PASTE FIX
    This repository provides a minimalist, extensible, and reproducible framework designed specifically for objectively benchmarking and comparing the accuracy and performance of various speech-to-text engines.
  • mediumtopics#2
    Add topics related to benchmarking and evaluation

    Why:

    CURRENT
    aws-transcribe, cheetah, deep-learning, deep-neural-networks, deepspeech, edge-ai, google-speech-to-text, mozilla-deepspeech, offline, picovoice, pocketsphinx, privacy, speech-recognition, speech-to-text, voice-recognition
    COPY-PASTE FIX
    aws-transcribe, cheetah, deep-learning, deep-neural-networks, deepspeech, edge-ai, google-speech-to-text, mozilla-deepspeech, offline, picovoice, pocketsphinx, privacy, speech-recognition, speech-to-text, voice-recognition, benchmark, benchmarking, evaluation, performance-testing, accuracy-testing, comparison-tool
  • lowreadme#3
    Add a sentence to the README intro clarifying primary audience and use case

    Why:

    COPY-PASTE FIX
    It is designed for developers and researchers who need to objectively evaluate and compare the accuracy, performance, and efficiency of various speech-to-text technologies.

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 Picovoice/speech-to-text-benchmark
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Appen
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Appen · recommended 1×
  2. Scale AI · recommended 1×
  3. Amazon Mechanical Turk (MTurk) · recommended 1×
  4. audacity/audacity · recommended 1×
  5. Python · recommended 1×
  • CATEGORY QUERY
    How can I objectively compare accuracy and performance of various speech recognition services?
    you: not recommended
    AI recommended (in order):
    1. Appen
    2. Scale AI
    3. Amazon Mechanical Turk (MTurk)
    4. Audacity (audacity/audacity)
    5. Python
    6. requests (psf/requests)
    7. jiwer (jitsi/jiwer)
    8. pywer (ghcollin/pywer)
    9. Google Cloud Speech-to-Text
    10. Amazon Transcribe
    11. Microsoft Azure Speech-to-Text
    12. OpenAI Whisper (openai/whisper)
    13. Deepgram
    14. AssemblyAI
    15. Rev.ai

    AI recommended 15 alternatives but never named Picovoice/speech-to-text-benchmark. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate offline speech-to-text engine efficiency and word error rate?
    you: not recommended
    AI recommended (in order):
    1. Whisper
    2. pyannote.audio
    3. Vosk
    4. Kaldi
    5. DeepSpeech
    6. HTK
    7. time
    8. resource

    AI recommended 8 alternatives but never named Picovoice/speech-to-text-benchmark. 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 Picovoice/speech-to-text-benchmark?
    pass
    AI named Picovoice/speech-to-text-benchmark explicitly

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

  • If a team adopts Picovoice/speech-to-text-benchmark in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Picovoice/speech-to-text-benchmark 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 Picovoice/speech-to-text-benchmark solve, and who is the primary audience?
    pass
    AI did not name Picovoice/speech-to-text-benchmark — 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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MARKDOWN (README)
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Picovoice/speech-to-text-benchmark — 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