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ARXIV:2603.11273 · ASR OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11273ASR OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines.
Opportunity summary
Pain This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to…
Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to…
ASR Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines.
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Paper Pack
10.48550/arXiv.2603.11273This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines.
Abstract
Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling. We integrate two classical algorithms, Shortest Job First (SJF) and Highest Response Ratio Next (HRRN), into vLLM and evaluate them under realistic and drifted workloads. On LibriSpeech test-clean, compared to baseline, SJF reduces median E2E latency by up to $73\%$ at high load, but increases $90$th-percentile tail latency by up to $97\%$ due to starvation of long requests. HRRN addresses this trade-off: it reduces median E2E latency by up to $28\%$ while bounding tail-latency degradation to at most $24\%$. These gains persist under workload drift, with no throughput penalty and $<0.1$\,ms scheduling overhead per request.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking und...
METHOD
Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling.
WHY NOW
ASR Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ASR Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper presents a novel duration-aware scheduling approach for improving latency in ASR serving pipelines.
Segment
ASR Optimization
Adoption evidence
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Commercial read
3.0/10 public viability
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missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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TIMELINE
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