Paper Citation Record · LEDGER

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge

As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2507.18051.

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

pith.paper-citation-record.v1
2507.18051 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:27.070910Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:23.192060Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:45:27.557256Z

Reference resolution

42 of 42 outbound references displayed

verified exact2
verified fuzzy24
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5109d340-43e8-4c8e-9ffe-06374adade8b · outbound

This paper cites Recently, there has been growing interest in combining LLMs with audio encoders, en- abling the models to process and understand audio modali- ties [6, 7, 8, 9, 10, 11].

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Recently, there has been growing interest in combining LLMs with audio encoders, en- abling the models to process and understand audio modali- ties [6, 7, 8, 9, 10, 11]

Reference 1

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Observation 0dfb4d47-140b-46b2-a8ed-8deb1a2b1db9 · outbound

This paper cites The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge

Reference 2

Resolution: malformed identifier; local_arxiv, observed 2026-08-06T14:45:27.629575Z

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Observation 5d775110-1ef9-4a06-950a-1414e79b7f4f · outbound

This paper cites Datasets We use a large corpus to train our model, totaling 180k hours, as shown in Table 1.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Datasets We use a large corpus to train our model, totaling 180k hours, as shown in Table 1

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:33.105266Z

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Observation 7bd1b50e-1044-4c5a-bf71-354710cda3e1 · outbound

This paper cites an unresolved cited work.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Unresolved cited work

Reference 4

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Observation fffbe01d-97e6-4919-bffa-b2f568792835 · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge LLaMA: Open and Efficient Foundation Language Models

Reference 5

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Observation cfc1d010-c7d2-4b1a-a87b-482318edd8d6 · outbound

This paper cites Introducing chatgpt,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Introducing chatgpt,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:32.581263Z

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Observation 52bb2a9c-25da-45f6-b483-6ca5e705639b · outbound

This paper cites Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning

Reference 7

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Observation 28041e31-5707-4627-afd5-1fa157c5d4f5 · outbound

This paper cites Lan- guage models are few-shot learners,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Lan- guage models are few-shot learners,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:23.778593Z

Source-reported events for the cited work

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Observation e65baf4f-a4aa-43b8-8ff1-79d3209d714b · outbound

This paper cites PaLM 2 Technical Report.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge PaLM 2 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:23.908071Z

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Observation 15bf3a22-e640-42a6-ac35-a7cebfa1ddc7 · outbound

This paper cites Qwen2-Audio Technical Report.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Qwen2-Audio Technical Report

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:24.551512Z

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Observation 06463cb4-f5b1-4d7c-ac65-6b8cf78fd1f2 · outbound

This paper cites Listen, think, and understand,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Listen, think, and understand,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:32.390503Z

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Observation 17bbea26-8b35-4c54-a625-422d306902df · outbound

This paper cites SALMONN: towards generic hearing abilities for large language models,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge SALMONN: towards generic hearing abilities for large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:32.181787Z

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Observation 3f8dff7b-4211-432a-912a-6a0aecb86f02 · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:24.360453Z

Source-reported events for the cited work

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Observation 0f621295-184c-4542-8607-9fd292aec52c · outbound

This paper cites Wavllm: Towards robust and adaptive speech large language model,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Wavllm: Towards robust and adaptive speech large language model,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:31.923638Z

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Observation 28c0212d-65f8-4799-9caa-4ca13afb055e · outbound

This paper cites Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition

Reference 15

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Observation 32579c91-6bec-4326-8dde-a4a24d216c10 · outbound

This paper cites E- chat: Emotion-sensitive spoken dialogue system with large lan- guage models,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge E- chat: Emotion-sensitive spoken dialogue system with large lan- guage models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:31.615396Z

Source-reported events for the cited work

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Observation daebb02b-03d7-4ea6-9408-6390ab429414 · outbound

This paper cites Prompt- ing large language models with speech recognition abilities,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Prompt- ing large language models with speech recognition abilities,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:31.366251Z

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Observation abfbe24e-4e76-4fe2-a6df-5aaf152ba1b2 · outbound

This paper cites On decoder-only architecture for speech-to-text and large language model integration,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge On decoder-only architecture for speech-to-text and large language model integration,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:31.173055Z

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Observation 67cee1df-1eb8-46de-a7c2-5a0a038666a7 · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

Reference 19

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Observation a8e10e73-6a3c-4b70-9f59-31dcc66b6264 · outbound

This paper cites Attention is all you need,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Attention is all you need,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:29.861719Z

Source-reported events for the cited work

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Observation c2a40359-1de6-4ed0-8891-c5c29938ee7e · outbound

This paper cites Robust speech recognition via large-scale weak su- pervision,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Robust speech recognition via large-scale weak su- pervision,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:30.924050Z

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Observation 1740bf53-f428-4acf-b6c7-4002450b954c · outbound

This paper cites Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:27.419040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8583cb70-0878-4e89-850c-cdb15dd14945 · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Scaling speech technology to 1, 000+ languages,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:30.544635Z

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Observation e3ecebeb-1db4-43b7-94fd-4fa84352cb52 · outbound

This paper cites Lora: Low-rank adaptation of large lan- guage models,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Lora: Low-rank adaptation of large lan- guage models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:30.146746Z

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Observation e90c6ac7-541e-4607-a8ff-36897dca7c6f · outbound

This paper cites GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement

Reference 25

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Observation f3bb7416-d714-480e-ad6e-3285b8bcc404 · outbound

This paper cites Qwen3 Technical Report.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Qwen3 Technical Report

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:25.579054Z

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Observation bbbea1d3-daa5-4663-98ef-63919ef87493 · outbound

This paper cites MSR- 86K: an evolving, multilingual corpus with 86, 300 hours of tran- scribed audio for speech recognition research,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge MSR- 86K: an evolving, multilingual corpus with 86, 300 hours of tran- scribed audio for speech recognition research,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:29.777252Z

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Observation 82786020-78f8-4738-83ba-96ad4d5467aa · outbound

This paper cites Com- mon voice: A massively-multilingual speech corpus,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Com- mon voice: A massively-multilingual speech corpus,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:29.592363Z

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Observation 9b9625b0-a0b5-41b2-822e-862b2cef0243 · outbound

This paper cites MLS: A large-scale multilingual dataset for speech research,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge MLS: A large-scale multilingual dataset for speech research,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:29.369100Z

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Observation 89298056-c8c7-4a4e-887a-00ebc84249df · outbound

This paper cites The fisher corpus: a resource for the next generations of speech-to-text,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge The fisher corpus: a resource for the next generations of speech-to-text,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:28.503271Z

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Observation 3aeb822d-7a4b-4a68-b6f2-775f3e162188 · outbound

This paper cites Emilia: An extensive, multilingual, and diverse speech dataset for large-scale speech generation,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Emilia: An extensive, multilingual, and diverse speech dataset for large-scale speech generation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:29.012624Z

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Observation c9f30bf0-4c0f-404a-94c5-ca55bd764167 · outbound

This paper cites The average WER is reduced by 3.6% compared to the original baseline.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge The average WER is reduced by 3.6% compared to the original baseline

Reference 32

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T14:45:32.945762Z

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Observation dcf92287-9c04-4773-97b2-d191d4ce13fd · outbound

This paper cites Opendatalab: Empowering general artificial intelligence with open datasets,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Opendatalab: Empowering general artificial intelligence with open datasets,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:28.813061Z

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Observation 75539b25-e724-4fb9-b893-1e75bff383f9 · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Lib- rispeech: An ASR corpus based on public domain audio books,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:28.661767Z

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Observation d5156ee4-8fc9-44d8-9f7d-ac6a252ac9bb · outbound

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

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

Reference 35

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Observation 63ac38bd-0606-47c4-8841-5fc2e2247696 · outbound

This paper cites OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:27.070910Z

Source-reported events for the cited work

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Observation 3259b15b-c866-40ff-a0c8-597a8cdd2385 · outbound

This paper cites Reazonspeech: A free and massive cor- pus for japanese asr,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Reazonspeech: A free and massive cor- pus for japanese asr,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:28.255794Z

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Observation 5cde0366-8bd5-4531-b737-3502dc8d79e1 · outbound

This paper cites Construction of a large-scale japanese asr corpus on tv recordings,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Construction of a large-scale japanese asr corpus on tv recordings,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:28.099032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d5b036b-fd34-4bee-90ba-3b2ce05fe765 · outbound

This paper cites Golos: Russian Dataset for Speech Research.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Golos: Russian Dataset for Speech Research

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:27.229289Z

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Observation b6ef063f-f903-4c64-ae0f-cc53f4397866 · outbound

This paper cites Ksponspeech: Korean spontaneous speech corpus for automatic speech recog- nition,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Ksponspeech: Korean spontaneous speech corpus for automatic speech recog- nition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:27.943351Z

Source-reported events for the cited work

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Observation 860f4c4f-497a-479d-bef6-e2ccefbae6ad · outbound

This paper cites Unsupervised cross-lingual representation learning for speech recognition,.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge Unsupervised cross-lingual representation learning for speech recognition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:27.769815Z

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Observation c535d169-99fa-47cc-926f-bb5f0e7e7c2a · outbound

This paper cites OpenDataLab: Empowering General Artificial Intelligence with Open Datasets.

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge OpenDataLab: Empowering General Artificial Intelligence with Open Datasets

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:26.301812Z

Source-reported events for the cited work

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Pith citing papers

Observation 0dfb4d47-140b-46b2-a8ed-8deb1a2b1db9 · inbound