Paper Citation Record · LEDGER

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2510.07355.

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

pith.paper-citation-record.v1
2510.07355 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:06:50.337605Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:56:58.344964Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

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verified fuzzy0
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External citation measurements

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Outbound references

Observation 8b51d3a7-2e73-4cf8-bf6d-d8a2ba21af06 · outbound

This paper cites Emotional communication in speech and music: The role of melodic and rhythmic contrasts,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Emotional communication in speech and music: The role of melodic and rhythmic contrasts,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:43.632154Z

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Observation 6d4917ed-eef9-49dc-b21f-0e0d0879834c · outbound

This paper cites Effects of variation in emotional tone of voice on speech perception,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Effects of variation in emotional tone of voice on speech perception,

Reference 2

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Observation 56007e68-f4c7-4b26-8fd2-92161abff7f0 · outbound

This paper cites Analysis of emotion recognition using facial ex- pressions, speech and multimodal information,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Analysis of emotion recognition using facial ex- pressions, speech and multimodal information,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:43.765144Z

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Observation 50d76174-2266-43d9-a942-edcaf98c9b63 · outbound

This paper cites Language models are few-shot learners,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Language models are few-shot learners,

Reference 4

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Observation 55ce28e0-4813-4353-8197-81b50a8f88de · outbound

This paper cites LLaMA-Omni: Seamless Speech Interaction with Large Language Models.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues LLaMA-Omni: Seamless Speech Interaction with Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.086529Z

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Observation 1b8bbee8-5951-4f8c-a92d-3c152e36ae7c · outbound

This paper cites Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.257304Z

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Observation 875ecdf8-0960-418d-b24a-1bc98129c738 · outbound

This paper cites Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.360493Z

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Observation 6d8c3881-632a-40ca-9478-ee325d43010f · outbound

This paper cites GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.532704Z

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Observation 88a46cbb-0276-4a0c-b631-cfcac641ba3a · outbound

This paper cites Moshi: a speech-text foundation model for real-time dialogue.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Moshi: a speech-text foundation model for real-time dialogue

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.649264Z

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Observation d21cad1c-f787-4e84-98ac-87728cf598ea · outbound

This paper cites EMO-Reasoning: Benchmarking Emotional Rea- soning Capabilities in Spoken Dialogue Systems,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues EMO-Reasoning: Benchmarking Emotional Rea- soning Capabilities in Spoken Dialogue Systems,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.765981Z

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Observation 39279745-9fba-4474-9f3e-652bed7a1193 · outbound

This paper cites Textually pretrained speech language mod- els,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Textually pretrained speech language mod- els,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:44.936563Z

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Observation f0f98697-da92-443a-b0aa-be673aa1316b · outbound

This paper cites GSQA: An End-to-End Model for Generative Spoken Question Answering.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues GSQA: An End-to-End Model for Generative Spoken Question Answering

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:45.071560Z

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Observation da687337-5354-4610-8d33-81c741879d91 · outbound

This paper cites Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback

Reference 13

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Observation 125e2bb9-743b-4c43-a38f-8fc9c0a22b07 · outbound

This paper cites Dynamic-superb: Towards a dynamic, collab- orative, and comprehensive instruction-tuning benchmark for speech,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Dynamic-superb: Towards a dynamic, collab- orative, and comprehensive instruction-tuning benchmark for speech,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:45.330196Z

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Observation 2e7e6682-6c59-46bc-a742-1b2da70970a0 · outbound

This paper cites Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

Reference 15

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Observation 5bcdd55d-b602-4683-9c78-67d02463c97b · outbound

This paper cites Sd-eval: A benchmark dataset for spoken dialogue understanding beyond words,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Sd-eval: A benchmark dataset for spoken dialogue understanding beyond words,

Reference 16

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Observation 294f0709-7083-4820-8032-f79d87d4813c · outbound

This paper cites Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:45.730320Z

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Observation d79b70b3-246a-4063-ad4d-4050923f06a6 · outbound

This paper cites Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?

Reference 18

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Observation 59b66f37-2dda-4843-acd9-3416ffaabd9e · outbound

This paper cites Paralinguistics-enhanced large language model- ing of spoken dialogue,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Paralinguistics-enhanced large language model- ing of spoken dialogue,

Reference 19

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Observation 8cf06290-04af-4d4b-b500-0508e399abb4 · outbound

This paper cites Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities

Reference 20

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Observation 393e8571-d257-4c2c-85f1-4bf558dec92d · outbound

This paper cites Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Models.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Models

Reference 21

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Observation 6a3bd3b4-2cb7-481c-9ffb-82fbbd7114b7 · outbound

This paper cites EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition

Reference 22

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Observation 1952de1b-9e3c-48fb-83b2-c9ab0b44afeb · outbound

This paper cites Dfme: A new benchmark for dynamic facial micro- expression recognition,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Dfme: A new benchmark for dynamic facial micro- expression recognition,

Reference 23

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Observation 650e59fa-b87e-4d33-996e-ec183875082e · outbound

This paper cites What comprises a good talking-head video generation?: A Survey and Benchmark.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues What comprises a good talking-head video generation?: A Survey and Benchmark

Reference 24

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Observation 8e28c4f6-9261-415c-b69f-acb4787ddc3d · outbound

This paper cites Subjective and objective quality-of-experience assessment for 3d talking heads,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Subjective and objective quality-of-experience assessment for 3d talking heads,

Reference 25

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Observation a6619faf-fe91-4fdc-91ca-efe844f7e6ac · outbound

This paper cites Av- data2vec: Self-supervised learning of audio-visual speech representa- tions with contextualized target representations,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Av- data2vec: Self-supervised learning of audio-visual speech representa- tions with contextualized target representations,

Reference 26

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Observation c84f1758-c514-4d74-adb5-c47ff4736a94 · outbound

This paper cites Jointly Learning From Unimodal and Multimodal-Rated Labels in Audio-Visual Emotion Recognition,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Jointly Learning From Unimodal and Multimodal-Rated Labels in Audio-Visual Emotion Recognition,

Reference 27

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Observation e491b503-38be-46cd-ae0d-99b8fc7f6426 · outbound

This paper cites Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance,

Reference 28

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This paper cites Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detection,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detection,

Reference 29

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This paper cites Let’s Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Let’s Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation,

Reference 30

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This paper cites FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

Reference 31

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Observation 8b2d7aaa-cd9f-4a57-8f0c-a142052a77cb · outbound

This paper cites Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction,

Reference 32

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This paper cites Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions,

Reference 33

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This paper cites Avec 2016: Depression, mood, and emotion recognition workshop and challenge,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Avec 2016: Depression, mood, and emotion recognition workshop and challenge,

Reference 34

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This paper cites Hicmae: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recognition,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Hicmae: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recognition,

Reference 35

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This paper cites Baichuan-Omni-1.5 Technical Report,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Baichuan-Omni-1.5 Technical Report,

Reference 36

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Observation 158e03a8-2b26-406f-8e84-9d8f6cbc1791 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 37

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Qwen2.5-Omni Technical Report

Reference 38

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues GPT-4 Technical Report

Reference 39

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

Reference 40

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This paper cites AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark,

Reference 41

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This paper cites DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models

Reference 42

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Observation 92494f21-2696-4f33-8c6e-89b5d4088fb3 · outbound

This paper cites Unconstrained dysfluency modeling for dys- fluent speech transcription and detection,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Unconstrained dysfluency modeling for dys- fluent speech transcription and detection,

Reference 43

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Observation 981e0606-c161-4694-be0a-c9722b56dde7 · outbound

This paper cites Towards hierarchical spo- ken language disfluency modeling,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Towards hierarchical spo- ken language disfluency modeling,

Reference 44

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This paper cites Ssdm: Scalable speech dysfluency model- ing,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Ssdm: Scalable speech dysfluency model- ing,

Reference 45

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Auto- matic detection of articulatory-based disfluencies in primary progres- sive aphasia,

Reference 46

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Yolo-stutter: End-to-end region-wise speech dysfluency detection,

Reference 47

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Stutter-solver: End-to-end multi- lingual dysfluency detection,

Reference 48

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Observation c0fd4617-7d21-4a44-9fa9-f104ec2ee1bf · outbound

This paper cites Time and tokens: Benchmarking end-to-end speech dys- fluency detection,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Time and tokens: Benchmarking end-to-end speech dys- fluency detection,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T11:06:49.590004Z

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Observation 5f5d49ad-c691-4f74-946f-1d5f811bd2bb · outbound

This paper cites Towards accurate phonetic error detection through phoneme similarity modeling,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Towards accurate phonetic error detection through phoneme similarity modeling,

Reference 50

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Observation ee530a01-f84e-46d9-a9e6-15199c6855d0 · outbound

This paper cites Dysfluent wfst: A framework for zero-shot speech dysfluency transcription and detection,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Dysfluent wfst: A framework for zero-shot speech dysfluency transcription and detection,

Reference 51

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Observation dbea160c-b9e7-43e5-828d-747d8831208d · outbound

This paper cites Analysis and evaluation of synthetic data generation in speech dysfluency detec- tion,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Analysis and evaluation of synthetic data generation in speech dysfluency detec- tion,

Reference 52

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Observation 6fab458a-3d21-45c4-9b3e-731303dd4d5f · outbound

This paper cites LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness

Reference 53

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Observation 7d32a70c-a167-4f3d-a62f-1c516f3e9576 · outbound

This paper cites Seamless dysfluent speech text alignment for disordered speech analysis,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Seamless dysfluent speech text alignment for disordered speech analysis,

Reference 54

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Observation 7f7edb46-3e57-4b63-9836-0d198d68fec6 · outbound

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues K-function: Joint pronunciation transcription and feedback for evaluating kids language function,

Reference 55

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Observation 50249b44-c621-400b-b776-11c39a144355 · outbound

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AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition,

Reference 56

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Observation b7dea22d-a56e-4a87-a507-4492bc2313e8 · outbound

This paper cites Articulatory representation learn- ing via joint factor analysis and neural matrix factorization,.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues Articulatory representation learn- ing via joint factor analysis and neural matrix factorization,

Reference 57

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Pith citing papers

Observation 4bbd6493-6c83-43e5-9f56-dba555af0d74 · inbound

S-MARC: Causal Streaming Reasoning for Full-Duplex Conversational Behavior Modeling cites this paper.

S-MARC: Causal Streaming Reasoning for Full-Duplex Conversational Behavior Modeling AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues

Reference 14

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