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MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition

As of 16 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2606.18228.

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

pith.paper-citation-record.v1
2606.18228 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

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measured 79 of 79 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

79 of 79 outbound references displayed

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External citation measurements

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Outbound references

Observation 757680e2-a735-454e-b821-44402f7f40ac · outbound

This paper cites Ai-powered chatbots as emotion-aware virtual assistants: Enhancing student support and engagement in higher education,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Ai-powered chatbots as emotion-aware virtual assistants: Enhancing student support and engagement in higher education,

Reference 1

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Observation 0997e9bc-eb2f-4108-9a75-9f9a404a889e · outbound

This paper cites emotion2vec: Self-supervised pre-training for speech emotion repre- sentation,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition emotion2vec: Self-supervised pre-training for speech emotion repre- sentation,

Reference 2

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Observation 4074abb3-65c8-479d-8044-0dda9e28bc5b · outbound

This paper cites Enhancing speech emotion recognition through a cross-dataset analysis: Exploring improved models,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Enhancing speech emotion recognition through a cross-dataset analysis: Exploring improved models,

Reference 3

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Observation a017f571-08a4-4c7d-85b4-0271501d5aaf · outbound

This paper cites Exploring the potential of convolutional neural networks in sequential data analysis: A comparative study with lstms and bilstms,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Exploring the potential of convolutional neural networks in sequential data analysis: A comparative study with lstms and bilstms,

Reference 4

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Observation 660abba8-a77c-4fe4-b687-715b95911ea4 · outbound

This paper cites Iemocap: Interactive emotional dyadic motion capture database,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Iemocap: Interactive emotional dyadic motion capture database,

Reference 5

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Observation 51d69b0b-01c1-4e76-aca1-d5f3e1d03f95 · outbound

This paper cites The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,

Reference 6

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Observation 77efc593-b339-4870-ac25-9fd415475294 · outbound

This paper cites Text-based emo- tion detection: Advances, challenges, and opportunities,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Text-based emo- tion detection: Advances, challenges, and opportunities,

Reference 7

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Observation 40b4a428-a469-49a1-b6e3-8faba54b39d2 · outbound

This paper cites A survey of state-of-the-art approaches for emotion recognition in text,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A survey of state-of-the-art approaches for emotion recognition in text,

Reference 8

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Observation d94cef6a-8015-47e4-ad39-563e55fae4d1 · outbound

This paper cites Kjeldsen: Design issues for vision-based computer... - google scholar,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Kjeldsen: Design issues for vision-based computer... - google scholar,

Reference 9

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Observation 77177e46-7863-450d-93e4-b8eb4fc056a8 · outbound

This paper cites The limitations for expression recognition in computer vision introduced by facial masks,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition The limitations for expression recognition in computer vision introduced by facial masks,

Reference 10

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Observation 2a938058-5853-43fc-985d-f5d24dc2d3e5 · outbound

This paper cites M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues,

Reference 11

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Observation ccfb7920-ab2c-4b59-9d2f-de6b327acf34 · outbound

This paper cites Deap: A database for emotion analysis; using physiological signals,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Deap: A database for emotion analysis; using physiological signals,

Reference 12

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Observation 6b54b2e2-2f3c-4a77-94ce-0a5022845ee7 · outbound

This paper cites A multimodal database for affect recognition and implicit tagging,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A multimodal database for affect recognition and implicit tagging,

Reference 13

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Observation a5e0432f-5f0f-4a7c-8153-8307e0efcc6f · outbound

This paper cites Decaf: Meg-based multimodal database for decoding affective physiological responses,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Decaf: Meg-based multimodal database for decoding affective physiological responses,

Reference 14

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Observation 8f3d583f-cee3-436f-ac15-891222ab0b8c · outbound

This paper cites Articulatory correlates of prosodic control: Emotion and emphasis,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Articulatory correlates of prosodic control: Emotion and emphasis,

Reference 15

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Observation 9f9955e8-dec1-4825-ab23-d9878f343c43 · outbound

This paper cites Measure- ments of articulatory variation in expressive speech for a set of swedish vowels,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Measure- ments of articulatory variation in expressive speech for a set of swedish vowels,

Reference 16

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Observation 6463b0f6-bc7d-44e4-ad60-d07baab04962 · outbound

This paper cites Articu- lation constrained learning with application to speech emotion recogni- tion,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Articu- lation constrained learning with application to speech emotion recogni- tion,

Reference 17

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Observation 4ebd54b9-55e4-49ab-bc1a-63c3b5990131 · outbound

This paper cites A study of emotional speech articulation using a fast magnetic resonance imaging technique.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A study of emotional speech articulation using a fast magnetic resonance imaging technique

Reference 18

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Observation 71345edb-8b32-4687-8f19-bc5347147ada · outbound

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

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Robust Speech Recognition via Large-Scale Weak Supervision

Reference 19

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Observation 6f171147-b62a-4c82-908a-63045e7b08ec · outbound

This paper cites bert-base-uncased-emotion,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition bert-base-uncased-emotion,

Reference 20

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Observation 9edbab7b-0c85-4009-8ed2-308c01edae6b · outbound

This paper cites A circumplex model of affect.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A circumplex model of affect

Reference 21

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Observation 9a465b36-23ac-4b6c-80d3-96cb422a9bf1 · outbound

This paper cites Muteit: Jaw motion based unvoiced command recognition using earable,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Muteit: Jaw motion based unvoiced command recognition using earable,

Reference 22

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Observation 400ba333-9685-4850-831b-628449ceee07 · outbound

This paper cites Jawthenticate: Microphone-free speech-based authentication using jaw motion and facial vibrations,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Jawthenticate: Microphone-free speech-based authentication using jaw motion and facial vibrations,

Reference 23

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Observation 412d6971-e46c-413b-b546-1cd34774cb3d · outbound

This paper cites Unvoiced: Designing an llm-assisted unvoiced user interface using earables,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Unvoiced: Designing an llm-assisted unvoiced user interface using earables,

Reference 24

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Observation 9e654e13-2278-486a-894b-44626248b1ce · outbound

This paper cites Face-mic: inferring live speech and speaker identity via subtle facial dynamics captured by ar/vr motion sensors,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Face-mic: inferring live speech and speaker identity via subtle facial dynamics captured by ar/vr motion sensors,

Reference 25

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Observation ae7a3154-451d-4591-b9cc-82fcc0ba8f88 · outbound

This paper cites Prosody in Phonetics: Definition and Examples,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Prosody in Phonetics: Definition and Examples,

Reference 26

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Observation 6561a0e8-2ce8-4ada-ae8c-a945c796376f · outbound

This paper cites A new approach of audio emotion recognition,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A new approach of audio emotion recognition,

Reference 27

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Observation 24dd37fc-af78-4a13-8004-6d1d2e201262 · outbound

This paper cites The acoustic cue of fear: investigation of acoustic param- eters of speech containing fear,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition The acoustic cue of fear: investigation of acoustic param- eters of speech containing fear,

Reference 28

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Observation 0811f08c-237b-4134-8a00-0c1ffb405555 · outbound

This paper cites Analysis of emotion recog- nition using facial expressions, speech and multimodal information,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Analysis of emotion recog- nition using facial expressions, speech and multimodal information,

Reference 29

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Observation 55596e0f-7898-45cf-8b48-786355a1a83a · outbound

This paper cites Control of coarticulatory patterns of tongue and jaw movement in speech,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Control of coarticulatory patterns of tongue and jaw movement in speech,

Reference 30

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Observation c80776ea-840a-48ed-85c0-ed794e1e8b8d · outbound

This paper cites An articu- latory study of emotional speech production.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition An articu- latory study of emotional speech production

Reference 31

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Observation 100df60b-9290-42a4-811e-bed41a19dfd4 · outbound

This paper cites Articulatory characteristics of expressive speech in activation-evaluation space,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Articulatory characteristics of expressive speech in activation-evaluation space,

Reference 32

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Observation 398f3fbc-3951-4cbd-ba41-547aa41f743c · outbound

This paper cites Articulatory-acoustic analyses of mandarin words in emotional context speech for smart campus,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Articulatory-acoustic analyses of mandarin words in emotional context speech for smart campus,

Reference 33

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Observation 34994827-67e5-4edb-9ef9-9f5321d67318 · outbound

This paper cites Emotion recog- nition involving physiological and speech signals: A comprehensive review,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion recog- nition involving physiological and speech signals: A comprehensive review,

Reference 34

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Observation 4057d159-8292-4396-9f07-15f70d43951e · outbound

This paper cites Jawsense: recognizing unvoiced sound using a low-cost ear-worn system,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Jawsense: recognizing unvoiced sound using a low-cost ear-worn system,

Reference 35

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Observation 9a25a143-a12f-40c1-816e-31209acfe18e · outbound

This paper cites Tramba: A hybrid transformer and mamba architecture for practical audio and bone conduction speech super resolution and enhancement on mobile and wearable platforms,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Tramba: A hybrid transformer and mamba architecture for practical audio and bone conduction speech super resolution and enhancement on mobile and wearable platforms,

Reference 36

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

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Observation 35e3b136-406f-4470-bc3a-eacc07ec0edf · outbound

This paper cites Rethinking gesture phases: Articulatory features of gestural movement?.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Rethinking gesture phases: Articulatory features of gestural movement?

Reference 37

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Observation 9db808f7-7d3e-4ad9-b831-4ddafbbe7d77 · outbound

This paper cites Specific respiratory pat- terns distinguish among human basic emotions,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Specific respiratory pat- terns distinguish among human basic emotions,

Reference 38

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:b91c43043cf4f0f4aac5d64a76e9a8c732ba2da2a831873ef9d8529ffd2dfd7b

Observation 6d567c54-6000-46f3-b121-2ef345edb314 · outbound

This paper cites Evaluation of gait smoothness in patients with stroke undergoing rehabilitation: Comparison between two metrics,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Evaluation of gait smoothness in patients with stroke undergoing rehabilitation: Comparison between two metrics,

Reference 39

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:8ee6700d58d29cdf6d241072d1b1056b62d08186aa5adbfe8e70cfcd90420bbb

Observation 4a56a4d0-84ce-456a-9345-768634e4b4b2 · outbound

This paper cites Spectral arc length as a method to quantify pharyngeal high-resolution manometric curve smoothness,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Spectral arc length as a method to quantify pharyngeal high-resolution manometric curve smoothness,

Reference 40

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:18ffc7a659e90a1b1cc3c0b3d3b19d7d0dbe610270ec0db148b0fd2453a1028a

Observation 23fdcc45-9d53-43eb-8a64-c101bb4847f4 · outbound

This paper cites Affect bursts as evolutionary precursors of speech and music,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Affect bursts as evolutionary precursors of speech and music,

Reference 41

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:8408d7506c83c0259c8b589909b71530d0138eab8b33cba119d1f4698cd9d738

Observation bc01fcdf-b064-4f20-8623-19b492b927a4 · outbound

This paper cites Validity and reliability of nonverbal voice measures as indicators of stressor-provoked anxiety,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Validity and reliability of nonverbal voice measures as indicators of stressor-provoked anxiety,

Reference 42

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:e66c1647063b698b9d76dbfbaf0791364a1b587c2e79ea9d59cee527085e3543

Observation 68421310-c299-4761-b18c-83893412405c · outbound

This paper cites Comparing fear and anxiety chemosignals: Do they modulate facial muscle activity and facilitate identifying facial expressions?.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Comparing fear and anxiety chemosignals: Do they modulate facial muscle activity and facilitate identifying facial expressions?

Reference 43

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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.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:b03f758f87aceae1e7d514f428f85edcec429b371c732dfee24432c21966095c

Observation 27d44506-02c4-4495-8eca-f0c99caafbf3 · outbound

This paper cites Emotion recognition in the noise applying large acoustic feature sets,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion recognition in the noise applying large acoustic feature sets,

Reference 44

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:9bb991e7a00b0fb6d58c427478aa605d1a6cec9be6b31c09ba49801f646657ca

Observation ddb2338e-d4ee-4290-8c8f-8b28a1e4f4a0 · outbound

This paper cites opensmile–the munich versatile and fast open-source audio feature extractor,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition opensmile–the munich versatile and fast open-source audio feature extractor,

Reference 45

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:b8ca4fd9883684154633f5bc996896763c8f60bff8f9a4d9d488c1e23f7cc6af

Observation 462ae446-5b39-45a8-acb2-fbf0183231a0 · outbound

This paper cites A comparison of machine learning algorithms and feature sets for automatic vocal emotion recognition in speech,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A comparison of machine learning algorithms and feature sets for automatic vocal emotion recognition in speech,

Reference 46

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:7047919852066ed6190a59d509e5f5c9ee11dfd70b841070db13e5d5f9e9c415

Observation 19957c49-9dcc-4d51-84f0-6a74af187b7f · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 47

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local_arxiv, observed 2026-07-03T23:19:03.673452Z

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.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:ede9c616567041dfa19d32d7478070cf14c97395cb665d9f31cea8f0691dd20d

Observation ed75b989-e633-4c77-8e80-39dbe4780284 · outbound

This paper cites A survey on multi-task learning,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A survey on multi-task learning,

Reference 48

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:138052f517ef98ecb1e035f27fc164b469c7a005fe598e211699bf8e683983b5

Observation 8f694f02-886c-4025-9690-8dbbeb26c541 · outbound

This paper cites Whispering wearables: Multimodal approach to silent speech recognition with head-worn devices,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Whispering wearables: Multimodal approach to silent speech recognition with head-worn devices,

Reference 49

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:f34025d239987b66aeda9e77df5af87440dd7e3b7bc1a793c7b297a062e14e08

Observation f5091445-bda1-45a3-ac7a-81037ecc562b · outbound

This paper cites Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning

Reference 50

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arxiv_id, observed 2026-07-03T23:19:03.665091Z

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.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:c256e571ce40a8358f87ba73c3bb98d38d62de380da8d843fa235471ac8c9542

Observation 97cb7051-8a6c-46d6-9de1-54b7dd8dc4f2 · outbound

This paper cites Fine-tuning the wav2vec2 model for automatic speech emotion recognition system,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Fine-tuning the wav2vec2 model for automatic speech emotion recognition system,

Reference 51

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:f584fce830bb99e188ef6c606fde7a422f2a4269c30b0ac41f27e98024b39f7c

Observation 31ab23e3-1076-4577-ba30-2e7ba1f12238 · outbound

This paper cites Speech based emotion recognition using machine learning,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Speech based emotion recognition using machine learning,

Reference 52

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:da7a602eb7d0e6779f9b7ea406b94e5fc3df2d76f6ebd4751a47d10162030999

Observation a9a17f16-828e-41f9-ac24-c99ca474643c · outbound

This paper cites A machine learning and deep learning based approach to generate a speech emotion recognition system,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A machine learning and deep learning based approach to generate a speech emotion recognition system,

Reference 53

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:edd4d4e3f2b0f1607458bce4ddb87ef237f17c5663a4fc9e805a343c44f1d2e5

Observation ef471211-8032-4e5f-8162-98f2b8ed5ca1 · outbound

This paper cites Speech emotion recognition using attention model,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Speech emotion recognition using attention model,

Reference 54

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:d4613d76d2928d4e95e4100ceae5a75380e03501264379037a66f1aae2a615b2

Observation 3b74ff6f-3e35-4115-a9ae-e9898a002612 · outbound

This paper cites Lstm based feature learning and cnn based classification for speech emotion recognition,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Lstm based feature learning and cnn based classification for speech emotion recognition,

Reference 55

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:e4c93ac8940563a880bc7578c1e5f8e426bb948efa52cd597adad9f0254d3b90

Observation 446ed57d-41ae-46e4-b26d-79036b2226bb · outbound

This paper cites A deep learning approach for speech emotion recognition optimization using meta-learning,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition A deep learning approach for speech emotion recognition optimization using meta-learning,

Reference 56

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:1304c6d89003ec2c6a17f4df3784fcd05a00ce3122c2bb65a8ad1e9983f74af0

Observation bd1ca313-31a4-4077-a32b-11c6166fd0bd · outbound

This paper cites Toronto emotional speech set (tess)-younger talker happy,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Toronto emotional speech set (tess)-younger talker happy,

Reference 57

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:b53fc787cba07e46779de6089a09c9084617426168f07cf54b381882cc55134c

Observation 57fd2118-a681-4d01-9ab8-305608766fac · outbound

This paper cites Speaker-dependent audio-visual emotion recognition.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Speaker-dependent audio-visual emotion recognition

Reference 58

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:89f639bab9a74b280e1e9e39ad37a27b8dcae112adfb5b8a4aa27b6d81a20a89

Observation 9347d2b4-61e2-425c-95ad-191660225d73 · outbound

This paper cites Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages

Reference 59

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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.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:4d0e8300404e65ed6044e0b30249055ad49d165dc370f5c0ebcfcdc2a937e895

Observation b50fe526-0f0b-43c8-bed5-f9c1477fb76b · outbound

This paper cites End-to-end speech emotion recognition: challenges of real-life emergency call centers data recordings,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition End-to-end speech emotion recognition: challenges of real-life emergency call centers data recordings,

Reference 60

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:1c28bf94548f59ad04419f6c7cbfb5ba8c09594ad51b1a5b186e8339facf73a0

Observation ad704800-5f1b-45d2-9e12-f16cb46cb264 · outbound

This paper cites Crema-d: Crowd-sourced emotional multimodal actors dataset,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Crema-d: Crowd-sourced emotional multimodal actors dataset,

Reference 61

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:ed21842fc3436728b3db7f598b2c4dc76cab6caadffb6afe5572fc8d3b18ff41

Observation 3da655eb-7f7c-4068-ac73-a85dff6114ca · outbound

This paper cites Cmu- mosei: A multimodal dataset for sentiment and emotion analysis,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Cmu- mosei: A multimodal dataset for sentiment and emotion analysis,

Reference 62

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:c7bbba180c996f718f8a5f21cb8fa1cebd77daa33166391337ab8590c429abd5

Observation e5c3bd3a-b661-4518-8107-d79300a44c5f · outbound

This paper cites Audio-visual emotion recognition using k-means clustering and spatio-temporal cnn,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Audio-visual emotion recognition using k-means clustering and spatio-temporal cnn,

Reference 63

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:0d44a7e6c461f89910f1c2b1dfe50b67a23360c698d7636139db3d32d463e414

Observation d1a644ea-5711-4e80-8336-16a480f65148 · outbound

This paper cites Sarcasm identification in textual data: systematic review, research challenges and open directions,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Sarcasm identification in textual data: systematic review, research challenges and open directions,

Reference 64

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:4dc83575ee87d46a0ddf64af9d49f55f9166e1a728db811393fe44897a792485

Observation 640c5679-13e6-425a-810f-2ce8db12cc5b · outbound

This paper cites Emotion recognition based on multi-modal physiological signals and transfer learning,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion recognition based on multi-modal physiological signals and transfer learning,

Reference 65

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:60c62f5f5076e841c8bd6fcfec6998d42af339938ceeedff88c3f3c1e9b93ef6

Observation 8fd7bb4d-ecb5-4ef4-bb81-11ddaea58e68 · outbound

This paper cites Emotion recognition with audio, video, eeg, and emg: a dataset and baseline approaches,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion recognition with audio, video, eeg, and emg: a dataset and baseline approaches,

Reference 66

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:1f182138e19606eff489c8a30f3b0595589f9232c5404729a55c26aad50191c7

Observation 2ca048aa-6635-463c-8314-c7d33f1a9942 · outbound

This paper cites Multimodal emotion recognition from expressive faces, body gestures and speech,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Multimodal emotion recognition from expressive faces, body gestures and speech,

Reference 67

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:cb9980720ee6d720da49d1750b187cffc12dd5488ff9d5049632bd68209dfb2f

Observation 5931f0e2-49e2-44ce-ac67-36eb7e4f0b0f · outbound

This paper cites Speech emotion based sentiment recognition using deep neural networks,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Speech emotion based sentiment recognition using deep neural networks,

Reference 68

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:e4f26207023ddaa322e662690a8e8e5ee4888b335394c5be181efe9af9bd93d7

Observation 7313befb-b7be-4525-999a-2543f747b5e9 · outbound

This paper cites Emotion detection from multilingual audio using deep analysis,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Emotion detection from multilingual audio using deep analysis,

Reference 69

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:ac5fb25a91dbcbca71f706691c35e2911e5700ec9e30602c116462e914c33d03

Observation 139aa366-ad40-4dac-a2e5-f8894351b2f4 · outbound

This paper cites Speech emotion recognition using mel-frequency cepstral coefficients & convolutional neural networks,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Speech emotion recognition using mel-frequency cepstral coefficients & convolutional neural networks,

Reference 70

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:bfd80d13217138103722f49e2895fdb7adb59a3e6caf1308e32506c248b03723

Observation ea27f027-7d80-41c9-acbb-5f241c932186 · outbound

This paper cites Multi-modal emotion recognition using eeg and speech signals,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Multi-modal emotion recognition using eeg and speech signals,

Reference 71

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:0068630ed32ef73bf00ce6abfe60a93e521c663615c0f724dab8c7a9ee2bff3b

Observation c3fff5a6-66bb-4438-bb61-b676638f5bd0 · outbound

This paper cites MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Reference 72

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local_arxiv, observed 2026-07-03T23:19:03.677917Z

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.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:0f67ecfdcc6026dc7f6c8f576b98c35259cfae3dd40fc1f9ac3dd9611c6677d4

Observation 74b07f38-9ed9-4df4-8b6c-df5dd62d9ca1 · outbound

This paper cites Surrey audio-visual expressed emotion (savee) database,.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Surrey audio-visual expressed emotion (savee) database,

Reference 73

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:8c853516cd5b55fb01caf6971190c443059d92bfcb26890c8ea86d7324a12dac

Observation a9bd07b0-ba03-41a6-9085-8235bdc62076 · outbound

This paper cites This data was used to train the system for our primary emotion recognition task.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition This data was used to train the system for our primary emotion recognition task

Reference 74

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Observation 42cf8d44-3c78-49d9-b7be-a27b9be4a77a · outbound

This paper cites an unresolved cited work.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Unresolved cited work

Reference 75

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:75fdbd487bfbfeb4e2ebdf30cf5f90615b6ad66d8b559cf5f9fab3f6e93a77bf

Observation ef450abb-1c70-40fc-8aa9-c7ce1593ac13 · outbound

This paper cites This enabled us to evaluate whether the system is content- and language-agnostic.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition This enabled us to evaluate whether the system is content- and language-agnostic

Reference 76

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:5e476c9016b23874732c06e081ab680f402e528fdf297f5e7e27f1259cace23d

Observation 0a4bd05a-a8b9-4fc4-aef0-ffcafa2c3ca4 · outbound

This paper cites This data was used to investigate how longer spoken sequences or snippets from familiar stories impact system performance.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition This data was used to investigate how longer spoken sequences or snippets from familiar stories impact system performance

Reference 77

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Observation 9e5a1c42-3a76-4301-a1ea-174db1b118cd · outbound

This paper cites These prompts were designed to encourage participants to express target emotions in a conversational setting while still relying on self-reported labels.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition These prompts were designed to encourage participants to express target emotions in a conversational setting while still relying on self-reported labels

Reference 78

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source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:43b6b7801c688529fdd44e1034f27f72e7d5cb268b8170c8e4c335c74448b863

Observation 758dc8e0-8e64-49be-8509-325211800c41 · outbound

This paper cites Each participant expressed each emotion 20 times.

MAJIC: Leveraging Articulatory Motion for Speech-based Emotion Recognition Each participant expressed each emotion 20 times

Reference 79

Resolution
unresolved
no resolver link, observed 2026-06-26T22:32:15.878239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T22:32:15.878239Z digest=sha256:ee670c705309dc042f4010ee3ab892355e5677030bcf592ce528435f93f8e265

Pith citing papers

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