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ARXIV:2603.16184 · MULTILINGUAL ASR · SUBMITTED 19 MAR · 20:22 UTC · FRESHNESS STALE
ARXIV:2603.16184MULTILINGUAL ASRSUBMITTED 19 MAR · 20:22 UTCFRESHNESS STALEarXiv
Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models.
Opportunity summary
Pain Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusively on publicly available speech corpora,…
We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. On 12 benchmarks spanning the four target languages, Polyglot-Lion-1.7B achieves an average error rate of 14.85, competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x…
Multilingual ASR moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models.
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Paper Pack
10.48550/arXiv.2603.16184Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models.
Abstract
We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusively on publicly available speech corpora, using a balanced sampling strategy that equalizes the number of training utterances per language and deliberately omits language-tag conditioning so that the model learns to identify languages implicitly from audio. On 12 benchmarks spanning the four target languages, Polyglot-Lion-1.7B achieves an average error rate of 14.85, competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x larger - while incurring a training cost of \$81 on a single RTX PRO 6000 GPU compared to \$18,862 for the 128-GPU baseline. Inference throughput is approximately 20x faster than MERaLiON at 0.10 s/sample versus 2.02 s/sample. These results demonstrate that linguistically balanced fine-tuning of moderate-scale pretrained models can yield deployment-ready multilingual ASR at a fraction of the cost of larger specialist systems.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
partial0 refs; 0 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusively on publicly available speech corpora, using a balanced samp...
METHOD
We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusi...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. On 12 benchmarks spanning the four target languages, Polyglot-Lion-1.7B achieves an average error rate of 14.85, competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x larger - while incurring a trainin...
WHY NOW
Multilingual ASR moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Polyglot-Lion-1.7B achieves an average error rate of 14.85
Explicitly stated in the abstract with specific numeric results
partial
competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x larger
Direct comparison with competitor model metrics provided in abstract
partial
incurring a training cost of $81 on a single RTX PRO 6000 GPU compared to $18,862 for the 128-GPU baseline
Specific cost figures directly stated in abstract
partial
Inference throughput is approximately 20x faster than MERaLiON at 0.10 s/sample versus 2.02 s/sample
Direct performance comparison with specific speed metrics
partial
using a balanced sampling strategy that equalizes the number of training utterances per language
Explicitly described as a core methodology in the abstract
partial
deliberately omits language-tag conditioning so that the model learns to identify languages implicitly from audio
Explicitly stated as a design choice in the abstract
partial
Limited to four languages (English, Mandarin, Tamil, Malay) without easy expansion
Explicitly stated as a caveat in the analysis section
partial
Relies on publicly available data which may not cover all accents or domains
Explicitly stated as a caveat in the analysis section
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
Polyglot-Lion offers efficient multilingual ASR tailored for Singapore's diverse languages at a fraction of the cost of larger models.
Segment
Multilingual ASR
Adoption evidence
Public code linked for build inspection
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Extension
Commercially relevant
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1/3 checks · 33%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.