- name
- senseaudio-pronunciation-coach
- description
- Foreign language pronunciation coach — listen to standard TTS pronunciation, record yourself, get word-by-word feedback on what was wrong, then practice targeted drills. Use when users want to improve pronunciation, practice speaking a foreign language, or ask for "发音练习", "跟读", "纠音", "外语口语练习", "pronunciation practice", "how to pronounce", or any request to check or improve spoken language accuracy.
- metadata
- openclaw
- requires
- env
- bins
- primaryEnv
- SENSEAUDIO_API_KEY
- homepage
- https://senseaudio.cn
- compatibility
- required_credentials
- description
- API key from https://senseaudio.cn/platform/api-key
- env_var
- SENSEAUDIO_API_KEY
SenseAudio Pronunciation Coach
Listen → Record → Compare → Drill. The loop that actually improves pronunciation.
Step 1: Choose Practice Material
Three input modes:
A — Direct input: User pastes a word, phrase, or sentence.
B — Scene presets: Offer these if the user isn't sure what to practice:
| Scene | Sample phrase |
|---|---|
| 机场值机 | "I'd like a window seat, please." |
| 餐厅点餐 | "Could I have the menu, please?" |
| 商务会议 | "Let me walk you through the agenda." |
| 酒店入住 | "I have a reservation under my name." |
| 购物 | "Do you have this in a different size?" |
| 问路 | "Excuse me, how do I get to the station?" |
C — Topic-based: User says "练习 th 发音" or "练习 r 和 l 的区别" — generate 5 sentences targeting that phoneme.
Also ask: 目标语言？ (default: English)
Step 2: Generate Standard Pronunciation
Produce two versions — slow for learning, normal for natural rhythm:
# Slow version (speed 0.75)
curl -s -X POST https://api.senseaudio.cn/v1/t2a_v2 \
-H "Authorization: Bearer $SENSEAUDIO_API_KEY" \
-H "Content-Type: application/json" \
-d "{
\"model\": \"SenseAudio-TTS-1.0\",
\"text\": \"<TEXT>\",
\"stream\": false,
\"voice_setting\": { \"voice_id\": \"<VOICE_ID>\", \"speed\": 0.75 },
\"audio_setting\": { \"format\": \"mp3\" }
}" -o slow.json
jq -r '.data.audio' slow.json | xxd -r -p > standard_slow.mp3
# Normal version (speed 1.0)
curl -s -X POST https://api.senseaudio.cn/v1/t2a_v2 \
-H "Authorization: Bearer $SENSEAUDIO_API_KEY" \
-H "Content-Type: application/json" \
-d "{
\"model\": \"SenseAudio-TTS-1.0\",
\"text\": \"<TEXT>\",
\"stream\": false,
\"voice_setting\": { \"voice_id\": \"<VOICE_ID>\", \"speed\": 1.0 },
\"audio_setting\": { \"format\": \"mp3\" }
}" -o normal.json
jq -r '.data.audio' normal.json | xxd -r -p > standard_normal.mp3Voice selection by language:
- English:
female_0006_a(clear, neutral accent) - Chinese:
female_0008_c(standard Mandarin) - Default:
female_0006_a
Tell the user: "慢速版和正常速版已生成。先听慢速版，感受每个音的发音，再听正常版感受自然节奏。准备好后，录一段你的跟读发给我。"
Step 3: Transcribe User Recording
When the user uploads their recording:
curl -s -X POST https://api.senseaudio.cn/v1/audio/transcriptions \
-H "Authorization: Bearer $SENSEAUDIO_API_KEY" \
-F "file=@<USER_RECORDING>" \
-F "model=sense-asr-pro" \
-F "response_format=verbose_json" \
-F "language=<LANGUAGE_CODE>" \
-F "timestamp_granularities[]=word" \
> asr_result.jsonLanguage codes: English → en, Chinese → zh, Japanese → ja, French → fr, Spanish → es
Extract the transcript: jq -r '.text' asr_result.json
Step 4: Word-by-Word Comparison (LLM task)
Compare the ASR transcript against the original text yourself. Align words and identify mismatches:
Comparison approach:
- Tokenize both original and ASR output into words
- Use sequence alignment (like diff) to match them
- Flag words where ASR output differs from original
Diagnosis output format:
跟读分析：
✓ "I'd like a" — 正确
✗ "window" — 识别为 "winder"（可能是 -ow 结尾发音问题）
✓ "seat" — 正确
✗ "please" — 识别为 "pleas"（末尾 -z 音可能不够清晰）
准确率：3/5 词 (60%)Common phoneme issues for Chinese speakers (English):
| Misrecognized as | Likely problem | Phoneme |
|---|---|---|
| "free" for "three" | th → f | /θ/ |
| "light" for "right" | r → l confusion | /r/ |
| "wery" for "very" | v → w | /v/ |
| "sit" for "seat" | short vs long vowel | /ɪ/ vs /iː/ |
| "fink" for "think" | th → f | /θ/ |
| dropped final consonant | final stop deletion | /t/, /d/, /k/ |
When a word is misrecognized, infer the likely phoneme issue and name it specifically.
Step 5: Targeted Drill
For each identified problem phoneme, generate a focused drill set:
Phoneme drill library:
| Phoneme | Drill words |
|---|---|
| /θ/ (th) | think, three, through, both, weather, teeth, breathe |
| /r/ | red, right, road, very, sorry, around, mirror |
| /r/ vs /l/ | right/light, road/load, rice/lice, pray/play |
| /v/ | very, voice, love, live, over, never, river |
| /iː/ vs /ɪ/ | seat/sit, beat/bit, sheep/ship, feel/fill |
| final /t/ | cat, hat, right, night, about, what, that |
| final /d/ | road, said, good, food, bad, head |
Present 3–5 drill words and generate slow TTS for each.
Step 6: Track Progress
Save session results to pronunciation_progress.json in the current directory:
{
"sessions": [
{
"date": "<ISO date>",
"text": "<practice text>",
"accuracy": 0.6,
"errors": ["window (/ow/)", "please (final /z/)"],
"phonemes_drilled": ["/ow/", "/z/"]
}
]
}After 3+ sessions, show a summary:
发音弱项分析（最近5次练习）：
/θ/ (th) ████████░░ 4次出错 ← 重点练习
/r/ ████░░░░░░ 2次出错
/iː/ ██░░░░░░░░ 1次出错
建议：重点练习 th 发音，可以说"把舌尖放在上下牙之间，轻轻吹气"。Iteration
After each round, ask: "再来一遍，还是换一个句子？" Keep the loop going until the user is satisfied or accuracy reaches 90%+.