Paper Citation Record · LEDGER

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture

As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2501.10666.

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

pith.paper-citation-record.v1
2501.10666 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:04:41.053883Z

measured 16 of 16 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:55:18.061325Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T00:35:10.333532Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy10
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4144881c-a92a-4420-b33b-2dda3b467629 · outbound

This paper cites The emotion detection services could enable users to have enhanced experiences to manipulate and adjust their real-time emotions, receiving feedback timely.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture The emotion detection services could enable users to have enhanced experiences to manipulate and adjust their real-time emotions, receiving feedback timely

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.319161Z

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 ac050e0f-d042-4c50-aae7-6305e4a8f608 · outbound

This paper cites Dataset description and preprocessing For the dataset, two relatively common-used datasets are taken into consideration as the original speech signal.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Dataset description and preprocessing For the dataset, two relatively common-used datasets are taken into consideration as the original speech signal

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.302489Z

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 64041510-569d-4326-9538-c27f1d87d443 · outbound

This paper cites The model loss for training and test set Firstly, the basic training effects of the model using the architectures designed above are illustrated in Figure 4.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture The model loss for training and test set Firstly, the basic training effects of the model using the architectures designed above are illustrated in Figure 4

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.284912Z

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 ce8db745-848a-47c9-8857-378e19902917 · outbound

This paper cites Regarding the feature extraction, features from four aspects are adopted for further analysis and the MFCC processing provided by the Librosa is specifically vital.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Regarding the feature extraction, features from four aspects are adopted for further analysis and the MFCC processing provided by the Librosa is specifically vital

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.268423Z

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 fbac4517-f616-4e4a-aa39-5e25e249d1cb · outbound

This paper cites an unresolved cited work.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:04:41.251130Z

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 f17e1887-6c35-46d8-b31a-bca48a40c738 · outbound

This paper cites 2015 Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios International journal of human-computer studies 82 pp 48-56.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios International journal of human-computer studies 82 pp 48-56

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.234302Z

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 b5209c21-5024-453f-aacb-153467950917 · outbound

This paper cites an unresolved cited work.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:04:41.216861Z

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 c2b51610-eee0-4f7a-be0e-d068c6f54496 · outbound

This paper cites 2015 Performance evaluation of different support vector machine kernels for face emotion recognition 2015 SAI Intelligent Systems Conference (IntelliSys) pp 804-806.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 Performance evaluation of different support vector machine kernels for face emotion recognition 2015 SAI Intelligent Systems Conference (IntelliSys) pp 804-806

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.200231Z

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 c60ab30b-18d1-4fdb-b357-f014d436cf1d · outbound

This paper cites 2015 Improved emotion recognition using GMM-UBMs 2015 International Conference on Signal Processing and Communication Engineering Systems pp 53-57.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 Improved emotion recognition using GMM-UBMs 2015 International Conference on Signal Processing and Communication Engineering Systems pp 53-57

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.181259Z

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 bef11215-8d8e-42c6-9b99-a328543a7165 · outbound

This paper cites an unresolved cited work.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:04:41.161639Z

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 51ce6d2d-c645-462f-8d79-95d42c766d33 · outbound

This paper cites 2015 librosa: Audio and music signal analysis in python Proceedings of the 14th python in science conference Vol 8 pp 18-25.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 librosa: Audio and music signal analysis in python Proceedings of the 14th python in science conference Vol 8 pp 18-25

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.145025Z

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 1c9e2c5b-228f-47b0-bb5a-e43d1d46fecc · outbound

This paper cites an unresolved cited work.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:04:41.128465Z

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 2f3a092d-0e79-4250-a45c-87f9b6bcbee2 · outbound

This paper cites 2015 Long-term recurrent convolutional networks for visual recognition and description Proceedings of the IEEE conference on computer vision and pattern recognition pp 2625- 2634.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 Long-term recurrent convolutional networks for visual recognition and description Proceedings of the IEEE conference on computer vision and pattern recognition pp 2625- 2634

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.111988Z

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 4abf6c28-5db3-4931-9364-7b7d3232e262 · outbound

This paper cites 2015 Classification of emotions of angry and disgust SmartCR 5(3) pp 151-158.

Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture 2015 Classification of emotions of angry and disgust SmartCR 5(3) pp 151-158

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:04:41.094805Z

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.

Pith citing papers

Observation 9274084a-bcfe-402f-8827-62114a6f2b12 · inbound

Low Resource Multimodal Translation of Nepali Spoken Words into Emotion-Conditioned Sign Language Avatars cites this paper.

Low Resource Multimodal Translation of Nepali Spoken Words into Emotion-Conditioned Sign Language Avatars Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:35:10.334848Z

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 9e87df13-b8c2-4bf1-a5e3-5575ff89b164 · inbound

Explainable Lightweight Compact Deep Models for Speech Emotion Recognition cites this paper.

Explainable Lightweight Compact Deep Models for Speech Emotion Recognition Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T19:55:18.061325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.