Paper Citation Record · LEDGER

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2505.21138.

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

pith.paper-citation-record.v1
2505.21138 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:19.259324Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:15.046428Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:45:19.754847Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation be2f8af5-9fd0-4e22-97c0-cc8d2b6f8bb1 · outbound

This paper cites Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis

Reference 1

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Observation 68bb04c9-6921-4b33-b68a-035357bc124a · outbound

This paper cites an unresolved cited work.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:45:23.181410Z

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Observation 6a06f114-ae7c-4d4f-a6d2-b34206c7dbd1 · outbound

This paper cites an unresolved cited work.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Unresolved cited work

Reference 3

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Observation 022ce5b0-9b80-4aaf-8976-9e10d90ce784 · outbound

This paper cites Comparison of Projector Architectures We evaluate the effectiveness of different projection layers through small-scale experiments.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Comparison of Projector Architectures We evaluate the effectiveness of different projection layers through small-scale experiments

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:45:19.680701Z

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Observation 3fd8d8ed-cbbf-4ac3-846a-c99f239000cf · outbound

This paper cites For the encoder, we employ Data2Vec2, pre-trained on 300,000 hours of unlabeled dialect and accented speech data.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis For the encoder, we employ Data2Vec2, pre-trained on 300,000 hours of unlabeled dialect and accented speech data

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.644271Z

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Observation 8481c00f-ee4f-4bd6-92ad-ce72ff14ed28 · outbound

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

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Zipformer: A faster and better encoder for automatic speech recognition,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:16.814667Z

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Observation 2bb44d2a-b5a4-476f-861b-2dbaced32437 · outbound

This paper cites Funasr: A fundamental end-to-end speech recognition toolkit,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Funasr: A fundamental end-to-end speech recognition toolkit,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.383789Z

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Observation eb57100b-7a45-4f87-9d32-bc852ae07b0b · outbound

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

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Robust Speech Recognition via Large-Scale Weak Supervision,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:15.733460Z

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Observation 5aa3de75-d092-4a8b-b414-25c96d26b77d · outbound

This paper cites Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:15.871141Z

Source-reported events for the cited work

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Observation d9a3c4c8-5151-4605-bfba-2b57057dfd21 · outbound

This paper cites Scaling speech technology to 1,000+ languages,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Scaling speech technology to 1,000+ languages,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:15.979645Z

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Observation cdcd4962-ea7b-47bc-a4f9-e667149a400a · outbound

This paper cites Wavlm: Large-scale self- supervised pre-training for full stack speech processing,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:16.086548Z

Source-reported events for the cited work

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Observation 7cf59c1a-c633-4c9a-9398-c5e63dc9fa20 · outbound

This paper cites WeNet 2.0: More Productive End- to-End Speech Recognition Toolkit,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis WeNet 2.0: More Productive End- to-End Speech Recognition Toolkit,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.507598Z

Source-reported events for the cited work

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Observation ddac4a9b-b1b5-4d92-a616-0ec24641fa9e · outbound

This paper cites HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.032796Z

Source-reported events for the cited work

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Observation 6feb6cd0-1b91-4d2a-ae45-6765904353d9 · outbound

This paper cites Data2vec: A general framework for self-supervised learning in speech, vision and language,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Data2vec: A general framework for self-supervised learning in speech, vision and language,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.877417Z

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Observation c6ace46f-5c53-421d-9584-04108fb150d8 · outbound

This paper cites Gpt-3: Its nature, scope, limits, and consequences,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Gpt-3: Its nature, scope, limits, and consequences,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.277132Z

Source-reported events for the cited work

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Observation 837f96f9-75ae-446f-b604-43ee8a776170 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis LLaMA: Open and Efficient Foundation Language Models

Reference 16

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Observation da5ae49d-881c-4b24-9167-04ee078dd221 · outbound

This paper cites GPT-4 Technical Report,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis GPT-4 Technical Report,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.162195Z

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Observation 127a1f90-fb97-4eeb-96c7-4f0727596302 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:17.750266Z

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Observation a44d2d83-e3e7-45c9-9599-bd49d8462d16 · outbound

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

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:17.875746Z

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Observation e2b84c9e-3a63-4c65-abe9-bb9003bdeb7a · outbound

This paper cites SALMONN: Towards Generic Hearing Abilities for Large Language Models,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis SALMONN: Towards Generic Hearing Abilities for Large Language Models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.588767Z

Source-reported events for the cited work

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Observation 0b967a9e-e2ff-4f9b-b058-7bc03bc552e6 · outbound

This paper cites An Embarrassingly Simple Approach for LLM with Strong ASR Capacity,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis An Embarrassingly Simple Approach for LLM with Strong ASR Capacity,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.434722Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c443ee92-6391-4879-a12b-0dd6be219cf4 · outbound

This paper cites Audiogpt: Understanding and generating speech, music, sound, and talking head,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Audiogpt: Understanding and generating speech, music, sound, and talking head,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:18.104596Z

Source-reported events for the cited work

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Observation 4887a9c0-72d9-4a75-b0a1-e439270cfc36 · outbound

This paper cites Leveraging large language models for exploiting asr uncer- tainty,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Leveraging large language models for exploiting asr uncer- tainty,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.751003Z

Source-reported events for the cited work

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Observation 65d8ca03-48b5-499a-a033-3562f9bc9b44 · outbound

This paper cites Can Generative Large Language Models Perform ASR Error Correction?.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Can Generative Large Language Models Perform ASR Error Correction?

Reference 24

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Observation e01d0042-4c78-4aab-9676-377ef9db2be8 · outbound

This paper cites MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition

Reference 25

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Observation 2b19b00f-8829-46ed-b7b4-22bf93a50ad4 · outbound

This paper cites Kespeech: An open source speech dataset of mandarin and its eight subdialects,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Kespeech: An open source speech dataset of mandarin and its eight subdialects,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:18.344386Z

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Observation cf602bb8-6d7f-4cbe-8291-7890086d3f75 · outbound

This paper cites AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.727866Z

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Observation 13b96e97-e6ec-4274-8db7-e12b36132b3b · outbound

This paper cites Qwen2.5 Technical Report.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Qwen2.5 Technical Report

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:18.861431Z

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Observation bbff2d03-474f-459b-8d98-60d1fda0ff5b · outbound

This paper cites Qwen-Audio: Advancing Universal Audio Understand- ing via Unified Large-Scale Audio-Language Models,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Qwen-Audio: Advancing Universal Audio Understand- ing via Unified Large-Scale Audio-Language Models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.285264Z

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Observation f42c58a3-cc26-4ab0-bcff-d2c2d5fa54b9 · outbound

This paper cites BEATs: Audio pre-training with acoustic tokenizers,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis BEATs: Audio pre-training with acoustic tokenizers,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.158546Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3695d0f6-299d-4d13-b43d-0ad63a1575ca · outbound

This paper cites Qwen2-Audio Technical Report.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Qwen2-Audio Technical Report

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:18.621065Z

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Observation 324bff1a-c214-4bd9-86d8-f4e6b34e9470 · outbound

This paper cites Unveiling the potential of llm-based asr on chinese open-source datasets,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Unveiling the potential of llm-based asr on chinese open-source datasets,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:21.025596Z

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Observation 5180bec7-511a-4a39-a479-48ddca99f3e1 · outbound

This paper cites WENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis WENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.876390Z

Source-reported events for the cited work

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Observation 688c2a28-a44b-4a5c-b59c-9e509b07c082 · outbound

This paper cites Why Gradient Clip- ping Accelerates Training: A Theoretical Justification for Adap- tivity,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Why Gradient Clip- ping Accelerates Training: A Theoretical Justification for Adap- tivity,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:19.937172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40d71125-3c70-4c54-ba84-d1b4db577188 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis LoRA: Low-Rank Adaptation of Large Language Models,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:19.259324Z

Source-reported events for the cited work

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Observation a455f803-08c6-4069-b4a2-5c9dad28b553 · outbound

This paper cites Tele- speechpt: Large-scale chinese multi-dialect and multi-accent speech pre-training,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Tele- speechpt: Large-scale chinese multi-dialect and multi-accent speech pre-training,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.589344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 52f0cbdb-4535-4bd9-aa5b-a0c5d249db96 · outbound

This paper cites Self-supervised learning with random-projection quantizer for speech recogni- tion,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Self-supervised learning with random-projection quantizer for speech recogni- tion,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.411187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2ecadf1c-0ca9-4529-b9a8-b370b758bc8b · outbound

This paper cites Di- nosr: Self-distillation and online clustering for self-supervised speech representation learning,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Di- nosr: Self-distillation and online clustering for self-supervised speech representation learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.245452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2cb9c4df-bbde-4824-aa06-9466f3fdc5ee · outbound

This paper cites Connecting speech encoder and large language model for asr,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Connecting speech encoder and large language model for asr,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:19.060669Z

Source-reported events for the cited work

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Observation 4849042a-9d3d-4c86-8198-726b2158e300 · outbound

This paper cites Decoupled Weight Decay Regular- ization,.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis Decoupled Weight Decay Regular- ization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:20.067738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a30bf01b-67fc-451e-82f2-1ff2a334620a · outbound

This paper cites However, lower down-sampling rates also increase the computational load on the LLM, requiring more resources during both training and in- ference.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis However, lower down-sampling rates also increase the computational load on the LLM, requiring more resources during both training and in- ference

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.810482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b0cd60e2-9d25-4963-a81e-8ed57ee145ff · outbound

This paper cites We configure LoRA with alpha = 32, rank = 12.

Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis We configure LoRA with alpha = 32, rank = 12

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:22.947544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation be2f8af5-9fd0-4e22-97c0-cc8d2b6f8bb1 · inbound