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posted an update 6 days ago
Open Pronunciation Assessment API — 17MB model, sub-300ms, phoneme-level scoring Hi everyone! I've been working on a pronunciation assessment engine optimized for edge deployment and real-time feedback. Wanted to share it with the community and get feedback. **What it does**: Scores English pronunciation at 4 levels of granularity — phoneme, word, sentence, and overall (0-100 each). Returns IPA and ARPAbet notation for every phoneme. **Key specs**: - 17MB total model size — runs entirely on CPU - 257ms median inference latency - Exceeds human inter-annotator agreement at phone-level (+4.5%) and sentence-level (+5.2%) - Benchmarked on standard academic datasets (2,500+ test utterances) - Validated across 7 L1 backgrounds (Chinese, Japanese, Korean, Arabic, Spanish, Vietnamese, Russian) **Architecture**: Proprietary ML pipeline optimized for pronunciation assessment. The entire engine runs in 17MB — no GPU required, no large foundation models needed. **Try it**: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment The demo lets you record audio or upload a file, enter the expected text, and get instant scoring down to individual phonemes. **API access**: Available via REST API, MCP servers (for AI agents), and Azure Marketplace. Details in the Space description. Would love feedback on: 1. Use cases you'd find this useful for 2. Languages you'd want supported next 3. Whether the scoring feels calibrated for your experience level Thanks!https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment
updated a Space 7 days ago
fabiosuizu/pronunciation-assessment
posted an update 9 days ago
Hi everyone! I've been working on a pronunciation assessment engine optimized for edge deployment and real-time feedback. Wanted to share it with the community and get feedback. **What it does**: Scores English pronunciation at 4 levels of granularity — phoneme, word, sentence, and overall (0-100 each). Returns IPA and ARPAbet notation for every phoneme. **Key specs**: - 17MB total model size (NeMo Citrinet-256, INT4 quantized) - 257ms median inference on CPU - Exceeds human inter-annotator agreement at phone-level (+4.5%) and sentence-level (+5.2%) - Benchmarked on speechocean762 (2,500 test utterances) - Tested across 7 L1 backgrounds (Chinese, Japanese, Korean, Arabic, Spanish, Vietnamese, Russian) **Architecture**: CTC forced alignment + Viterbi decoding + GOP (Goodness of Pronunciation) scoring + MLP/XGBoost ensemble heads. No wav2vec2 dependency — the entire pipeline runs in 17MB. **Try it**: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment The demo lets you record audio or upload a file, enter the expected text, and get instant scoring down to individual phonemes. **API access**: Available via REST API, MCP servers (for AI agents), and Azure Marketplace. Details in the Space description. Would love feedback on: 1. Use cases you'd find this useful for 2. Languages you'd want supported next 3. Whether the scoring feels calibrated for your experience level Thanks!
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