Paper Citation Record · LEDGER

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 3 inbound Pith citation observations for arXiv:2505.21245.

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

pith.paper-citation-record.v1
2505.21245 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:22.463974Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:17.627464Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:00:36.671800Z

Reference resolution

45 of 45 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfb7af36-e904-437e-b18f-0dafb1d8d1e9 · outbound

This paper cites However, performance progress tends to accompany an increasing number of model parameters and the need for computation and storage resources [7].

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision However, performance progress tends to accompany an increasing number of model parameters and the need for computation and storage resources [7]

Reference 1

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58fa2f1c-41f9-4086-8d6e-b92e68a5b606 · outbound

This paper cites Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:17.627464Z

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Observation fc31bfd2-0df8-468d-8010-b0024d83df23 · outbound

This paper cites Conformer) is a popular E2E ASR architecture that achieves state-of-the-art performance on many speech recognition tasks [1].

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Conformer) is a popular E2E ASR architecture that achieves state-of-the-art performance on many speech recognition tasks [1]

Reference 3

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Observation 40ff06b2-2dd8-48dd-a1b9-00a0458760cd · outbound

This paper cites an unresolved cited work.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Unresolved cited work

Reference 4

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Observation 40d89875-2cee-40cb-bc30-810b485c28d5 · outbound

This paper cites Experimental Setup We conduct experiments on two commonly used ASR datasets:.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Experimental Setup We conduct experiments on two commonly used ASR datasets:

Reference 5

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Observation f5bf84ac-c73a-4a40-b7b2-1f870dea6eae · outbound

This paper cites espnet/egs2.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision espnet/egs2

Reference 6

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Observation 0fabc46b-f9bb-47a8-8d65-3183379724f6 · outbound

This paper cites We achieved performance-lossless 2-bit and 1-bit quantization of Conformer systems.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision We achieved performance-lossless 2-bit and 1-bit quantization of Conformer systems

Reference 7

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Observation 1139263b-93f7-45fb-bcbd-949a08caaabd · outbound

This paper cites 14200220, 14200021, 14200324 and Innovation Technology Fund grant No.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision 14200220, 14200021, 14200324 and Innovation Technology Fund grant No

Reference 8

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Observation 4d1b62d6-40b4-4ee5-9a4e-18ccf2601336 · outbound

This paper cites Conformer: Convolution- augmented transformer for speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Conformer: Convolution- augmented transformer for speech recognition,

Reference 9

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Observation b256af62-bb36-426a-94a5-d317295ff7ae · outbound

This paper cites Branchformer: Parallel MLP-attention architectures to capture local and global context for speech recognition and understanding,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Branchformer: Parallel MLP-attention architectures to capture local and global context for speech recognition and understanding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:29.096736Z

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Observation bfef0b33-801d-4897-a6c4-de8ddf06cf4d · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 11

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Observation f60906ee-61ea-4614-9cf0-1e43f4729880 · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 12

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Observation 85fe9dfe-ec92-4ad5-a523-3dfd362798f9 · outbound

This paper cites Zipformer: A faster and better encoder for automatic speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Zipformer: A faster and better encoder for automatic speech recognition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:28.703874Z

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Observation cdc826eb-6225-4c4c-b637-dd66b2a48353 · outbound

This paper cites Hybrid CTC/attention architecture for end-to-end speech recog- nition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Hybrid CTC/attention architecture for end-to-end speech recog- nition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:28.536573Z

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Observation 460f0035-df1f-4ca1-8bd3-8a377f4e402b · outbound

This paper cites Efficient Speech Representation Learning with Low-Bit Quantization.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Efficient Speech Representation Learning with Low-Bit Quantization

Reference 15

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Observation faeb25b3-5f7d-4c4c-91b5-46bc9d349790 · outbound

This paper cites A survey of quantization methods for efficient neu- ral network inference,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision A survey of quantization methods for efficient neu- ral network inference,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:28.232850Z

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Observation f01857c3-c2a1-49a7-8840-8d482b0b4269 · outbound

This paper cites Binarized neural networks,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Binarized neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:28.026739Z

Source-reported events for the cited work

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Observation 63ba8edc-f4d4-4f41-9766-b3022558da16 · outbound

This paper cites XNOR- Net: Imagenet classification using binary convolutional neural networks,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision XNOR- Net: Imagenet classification using binary convolutional neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.846054Z

Source-reported events for the cited work

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Observation 724bd2c4-dcc3-4403-b3ba-57815e6b02d2 · outbound

This paper cites Towards accurate binary convolu- tional neural network,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Towards accurate binary convolu- tional neural network,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.590242Z

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Observation a422b608-e969-4bba-8e51-a75b0e78b5bd · outbound

This paper cites Bi-Real Net: Enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algo- rithm,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Bi-Real Net: Enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algo- rithm,

Reference 20

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Observation e3ffaab8-7c17-4ee1-9d78-876c4248baf4 · outbound

This paper cites Accurate and efficient 2-bit quantized neural networks,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Accurate and efficient 2-bit quantized neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.121282Z

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Observation 6aa75516-0fdf-498d-9b6a-66664289a1ce · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 22

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Observation 7ac0e8c3-8980-4f1a-b4ed-17e7271a73a8 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

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Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision BiT: Robustly binarized multi- distilled transformer,

Reference 24

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Observation d9aad0fc-f122-4e52-bbee-371db6c014d0 · outbound

This paper cites BinaryBERT: Pushing the limit of bert quantization,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision BinaryBERT: Pushing the limit of bert quantization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.562199Z

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Observation 1752829b-8c91-41f0-a284-bf0b22fa859d · outbound

This paper cites Binary deep neural networks for speech recognition.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Binary deep neural networks for speech recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.251127Z

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Observation 30bd7d4e-2dfa-4189-83d3-62b846ec364b · outbound

This paper cites Binary neural networks for speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Binary neural networks for speech recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.051167Z

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Observation e568158a-d73a-4c04-8a51-38e4fcd94763 · outbound

This paper cites 4-bit conformer with native quantization aware training for speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision 4-bit conformer with native quantization aware training for speech recognition,

Reference 28

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Observation 37742126-4412-48d1-ab85-50d86458ca84 · outbound

This paper cites Integer- only zero-shot quantization for efficient speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Integer- only zero-shot quantization for efficient speech recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:25.593675Z

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Observation 504c4179-fb13-45c9-a2e1-8b465b9c1770 · outbound

This paper cites Lossless 4-bit quantization of architecture compressed conformer asr systems on the 300-hr switchboard corpus,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Lossless 4-bit quantization of architecture compressed conformer asr systems on the 300-hr switchboard corpus,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:25.296765Z

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Observation d19758bc-0c88-4566-8acc-ec4f7b76ee4c · outbound

This paper cites 4-bit quantization of LSTM-based speech recognition models,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision 4-bit quantization of LSTM-based speech recognition models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:25.224851Z

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Observation 220843e9-8579-4d33-a4d5-fd4222bdcf74 · outbound

This paper cites Mixed precision quan- tization of transformer language models for speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Mixed precision quan- tization of transformer language models for speech recognition,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:25.062269Z

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Observation d5f2d557-1cba-4475-b231-1ea685315310 · outbound

This paper cites One-pass multiple conformer and founda- tion speech systems compression and quantization using an all-in- one neural model,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision One-pass multiple conformer and founda- tion speech systems compression and quantization using an all-in- one neural model,

Reference 33

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Observation b68f287a-4419-4ea4-b7b1-77915e6c5696 · outbound

This paper cites Mixed precision low- bit quantization of neural network language models for speech recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Mixed precision low- bit quantization of neural network language models for speech recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:24.647873Z

Source-reported events for the cited work

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Observation b37e05fc-6512-4f90-be11-92578e46a2ab · outbound

This paper cites USM-Lite: Quantization and sparsity aware fine-tuning for speech recogni- tion with universal speech models,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision USM-Lite: Quantization and sparsity aware fine-tuning for speech recogni- tion with universal speech models,

Reference 35

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Observation 2dfdb50b-6109-439c-8882-3fad39c3c32e · outbound

This paper cites 2-bit conformer quantization for automatic speech recog- nition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision 2-bit conformer quantization for automatic speech recog- nition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:24.295566Z

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Observation e82d3d6a-d321-4be0-80ab-f4640ce74fa3 · outbound

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Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision USM RNN-T model weights binarization,

Reference 37

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Observation cd4ba8f7-a81c-4b43-b2f2-d82cc3c9295e · outbound

This paper cites Compressed MoE asr model based on knowledge distillation and quantization,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Compressed MoE asr model based on knowledge distillation and quantization,

Reference 38

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Observation a1767f14-4762-4326-8a36-b5c82a9d822b · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 39

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Observation 111c15a0-f846-4451-a01d-136ef9f367d4 · outbound

This paper cites Co-training 2L submodels for visual recognition,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Co-training 2L submodels for visual recognition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:23.530009Z

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Observation 1fd0ae67-49c5-4927-80de-3c8f346f909e · outbound

This paper cites QKD: Quantization-aware Knowledge Distillation.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision QKD: Quantization-aware Knowledge Distillation

Reference 41

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Observation f056287d-49e8-43b8-b2c1-e79334ba86d6 · outbound

This paper cites SWITCH- BOARD: Telephone speech corpus for research and develop- ment,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision SWITCH- BOARD: Telephone speech corpus for research and develop- ment,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:23.258319Z

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Observation 90504003-ba90-4572-841e-f60d8cf6a93c · outbound

This paper cites Lib- riSpeech: An ASR corpus based on public domain audio books,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Lib- riSpeech: An ASR corpus based on public domain audio books,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:23.045184Z

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Observation 501930b1-7d83-4c3d-a1c1-239a69651536 · outbound

This paper cites ESPnet: End-to-End Speech Processing Toolkit.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision ESPnet: End-to-End Speech Processing Toolkit

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:22.368716Z

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Observation 572ba3cf-b209-4ef5-9232-4610b59f2fc5 · outbound

This paper cites Some statistical issues in the comparison of speech recognition algorithms,.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision Some statistical issues in the comparison of speech recognition algorithms,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:22.463974Z

Source-reported events for the cited work

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Pith citing papers

Observation 58fa2f1c-41f9-4086-8d6e-b92e68a5b606 · inbound