Paper Citation Record · LEDGER

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models

As of 10 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2507.07877.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.07877 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:33:18.648556Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T19:11:30.638672Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-09T06:00:36.633615Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy4
unresolved16
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6bc46688-aa9c-4cc9-8319-b95a644da6ff · outbound

This paper cites Common Voice: A Massively-Multilingual Speech Corpus.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Common Voice: A Massively-Multilingual Speech Corpus

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:16.667135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad1243e9-dcf7-459e-8899-8dde1d289ec0 · outbound

This paper cites QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:16.723597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974c9036-f0f1-4a93-9b4f-3c8053387e9b · outbound

This paper cites Open ASR Leaderboard.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Open ASR Leaderboard

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:19.971652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9d34e85c-b8e3-4809-b6f5-2c8cc4fb1007 · outbound

This paper cites Bayesian Bits: Unifying Quantization and Pruning.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Bayesian Bits: Unifying Quantization and Pruning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:16.873346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b71415f-4c51-4d89-a66a-02a530104bb8 · outbound

This paper cites GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:16.997764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64a2df6b-06ee-4ca4-a48d-72ea2979080e · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.108140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6335c89f-b27a-495d-b4fb-d05ac86b0103 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.221423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc713a0-e0e9-4420-9e08-b5a828f761f5 · outbound

This paper cites LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.344109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b8c1342-1a83-4d70-b0be-ea65bc6bd6df · outbound

This paper cites TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.474056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ebdf28-84f2-4da2-a81e-22f839345aa3 · outbound

This paper cites Moonshine: Speech Recognition for Live Transcription and Voice Commands.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Moonshine: Speech Recognition for Live Transcription and Voice Commands

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.611799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38b90115-96ec-46e2-b2e2-392f4ebfd47a · outbound

This paper cites Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:33:18.981753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1cede3b5-eac3-48a3-bf2d-db4b57c70020 · outbound

This paper cites Evaluating Quantized Large Language Models.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Evaluating Quantized Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.787323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf0ce28b-93f8-4ae0-bd95-7dff8629a7f6 · outbound

This paper cites TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.842506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f45361a-3b52-4d69-adf5-48d6750ecff2 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:17.958746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 406cdbe9-e20f-46f3-b4c7-3f0e2e0fe0f3 · outbound

This paper cites SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted end-to-end Speech Recognition.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted end-to-end Speech Recognition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:19.830910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57773d97-b631-4f75-a3dd-ad027fed0e9e · outbound

This paper cites LIBRISPEECH: An ASR Corpus Based on Public Domain Audio Books.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models LIBRISPEECH: An ASR Corpus Based on Public Domain Audio Books

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:19.703957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfd33c6d-b74f-4e6d-bd96-6d64dce2979e · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Robust Speech Recognition via Large-Scale Weak Supervision

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:18.191288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7b34894-504f-44e4-9ae6-b92691c24421 · outbound

This paper cites Earnings-22: A Practical Benchmark for Accents in the Wild.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Earnings-22: A Practical Benchmark for Accents in the Wild

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:18.252620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2473759d-b5e1-4cfc-ab7e-6b114e6d14b9 · outbound

This paper cites Recognition and Understanding of Meetings - The AMI and AMIDA Projects.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Recognition and Understanding of Meetings - The AMI and AMIDA Projects

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:19.516753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de22dfab-7323-441f-8414-cfbad6ee18d5 · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:18.407339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9d6ea3-8a1f-4ed8-8865-22e14f8480c2 · outbound

This paper cites VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:18.489814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c44582e-8b3c-4947-8ccd-d9d6765b47ff · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 22

Resolution: malformed identifier; no resolver link, observed 2026-08-06T18:33:18.573489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe2286c9-b89a-4d60-ba74-12fbec1b1868 · outbound

This paper cites Note that weight clipping is not applied to TesseraQ in this set of experiments.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Note that weight clipping is not applied to TesseraQ in this set of experiments

Reference 512

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:33:19.338482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 08061e0a-b685-4613-802d-cbe119aae866 · inbound

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection cites this paper.

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:00:36.638121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.