Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T13:48:38.823233Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2412.00055.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T13:48:38.823233Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-16T17:00:59.421441Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T17:01:07.560848Z
50 of 50 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation fbae8e13-4575-4a35-8732-547c56753194 · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Machine Learning for Synthetic Data Generation: A Review
Reference 1
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Observation eb50a450-75af-40a0-b41f-70721610b531 · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Fonseca and F
Reference 2
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Observation adf24a7d-a0ae-4700-ab55-9a57e3090ec1 · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
Reference 3
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Observation 94354cc2-9b98-4316-9b17-341521a4bd2c · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR ArchiSound: Audio Generation with Diffusion
Reference 4
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Observation a15ce408-4235-4ed1-add6-2f2d4331c2f8 · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR SynthASR: Unlocking Synthetic Data for Speech Recognition
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Amazon Transcribe
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Microsoft Azure Speech-to-Text
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Voice EHR: Introducing Multimodal Audio Data for Health
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
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Observation 9e8f07bb-df5c-433e-9610-6d8c61bdf9fd · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Robust Speech Recognition via Large-Scale Weak Supervision
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
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Reference 18
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Mamba for Streaming ASR Combined with Unimodal Aggregation
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Observation b332acd8-fb36-401b-928c-990ae94df139 · outbound
Reference 20
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Reference 21
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Gambs, M.-O
Reference 23
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Reference 24
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR https://www.mims.com/india
Reference 25
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm
Reference 26
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR https://www.icd10data.com/ICD10CM/Codes
Reference 27
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR https://cloud.google.com/
Reference 28
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Observation 574e2ff1-8666-4cd6-ab7a-942253677e80 · outbound
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Scikit-learn: Machine Learning in Python
Reference 29
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Observation b7d2916b-7cac-44d1-828f-02a997daa595 · outbound
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Panayotov, G
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Observation 937fd7dc-25bb-4170-baa8-2ddbda9d014f · outbound
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Reference 34
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Reference 35
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Reference 37
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Observation dd532297-1ccd-4972-9416-eb3cf8ca3de4 · outbound
Reference 38
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Observation b1b8f862-b3da-4b28-81d0-21615656b7de · outbound
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Reference 39
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR M*Modal Fluency for Transcription - Next-generation Clinical Documentation
Reference 40
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR Google Speech-to-Text
Reference 41
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High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
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