Evidence Receipt. Related Resources.
Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/polyglot-lion-efficient-multilingual-asr-for-singapore-via-balanced-fine-tuning-of-qwen3-asr
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 8/10
- Last proof check
- 2026-03-19
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 50%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR
Canonical ID polyglot-lion-efficient-multilingual-asr-for-singapore-via-balanced-fine-tuning-of-qwen3-asr | Route /signal-canvas/polyglot-lion-efficient-multilingual-asr-for-singapore-via-balanced-fine-tuning-of-qwen3-asr
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/polyglot-lion-efficient-multilingual-asr-for-singapore-via-balanced-fine-tuning-of-qwen3-asrMCP example
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Dimensions overall score 8.0
GitHub Code Pulse
CachedClaim map
- Evidencepartial
Polyglot-Lion-1.7B achieves an average error rate of 14.85
ImplicationpartialExplicitly stated in the abstract with specific numeric results
Verificationpartialpartial
- Evidencepartial
competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x larger
ImplicationpartialDirect comparison with competitor model metrics provided in abstract
Verificationpartialpartial
- Evidencepartial
incurring a training cost of $81 on a single RTX PRO 6000 GPU compared to $18,862 for the 128-GPU baseline
ImplicationpartialSpecific cost figures directly stated in abstract
Verificationpartialpartial
- Evidencepartial
Inference throughput is approximately 20x faster than MERaLiON at 0.10 s/sample versus 2.02 s/sample
ImplicationpartialDirect performance comparison with specific speed metrics
Verificationpartialpartial
- Evidencepartial
using a balanced sampling strategy that equalizes the number of training utterances per language
ImplicationpartialExplicitly described as a core methodology in the abstract
Verificationpartialpartial
- Evidencepartial
deliberately omits language-tag conditioning so that the model learns to identify languages implicitly from audio
ImplicationpartialExplicitly stated as a design choice in the abstract
Verificationpartialpartial
- Evidencepartial
Limited to four languages (English, Mandarin, Tamil, Malay) without easy expansion
ImplicationpartialExplicitly stated as a caveat in the analysis section
Verificationpartialpartial
- Evidencepartial
Relies on publicly available data which may not cover all accents or domains
ImplicationpartialExplicitly stated as a caveat in the analysis section
Verificationpartialpartial
