Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:44:49.936719Z
Paper Citation Record · LEDGER
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning
As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2508.03046.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
47 of 47 outbound references displayed
- verified exact0
- verified fuzzy46
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 09d03914-c9b1-4c58-b759-fab8412e2b16 · outbound
This paper cites First, deep learning has become central to speech sep- aration algorithms [1, 2, 3, 4], which typically require large, energy intensive resources like GPUs.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning First, deep learning has become central to speech sep- aration algorithms [1, 2, 3, 4], which typically require large, energy intensive resources like GPUs
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.074787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8de63e9f-e7be-4f51-a373-9eedadf1c48c · outbound
This paper cites TF-MLPNet: Tiny Real-Time Neural Speech Separation.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning TF-MLPNet: Tiny Real-Time Neural Speech Separation
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:44:45.362442Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63237ff3-3bac-492b-813d-edca2cb467c3 · outbound
This paper cites Neural target speech extraction: An overview,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Neural target speech extraction: An overview,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.066401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 36751e47-8578-4b6d-93db-ec536190a27a · outbound
This paper cites Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.058220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 210c5792-1af4-4423-9158-dae32ea26e21 · outbound
This paper cites Attention is all you need in speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Attention is all you need in speech separation,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.049974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 301e0c24-4177-4283-9fad-160d61167fcc · outbound
This paper cites Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.042149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c9102cbd-59f4-4bdc-a775-8f11bb3f97fb · outbound
This paper cites Se- mantic hearing: Programming acoustic scenes with binaural hear- ables,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Se- mantic hearing: Programming acoustic scenes with binaural hear- ables,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.033956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 584f4611-6efb-4eb6-883e-47721a3194f1 · outbound
This paper cites Look once to hear: Target speech hearing with noisy examples,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Look once to hear: Target speech hearing with noisy examples,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.026031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation edbdcfbc-8453-4bf8-836a-a74afd3cdd81 · outbound
This paper cites Multi-channel target speaker extraction with refine- ment: The wavlab submission to the second clarity enhancement challenge,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Multi-channel target speaker extraction with refine- ment: The wavlab submission to the second clarity enhancement challenge,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.017799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 39cdead7-a866-434c-9245-62dbdd9a0ccb · outbound
This paper cites Hearable devices with sound bubbles,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Hearable devices with sound bubbles,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.009115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2c4428de-335a-4f54-b9c6-30b42d8c0ec2 · outbound
This paper cites NDP120 – Syntiant,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning NDP120 – Syntiant,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:51.000701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation acabaeb6-c678-4a53-8409-1f35c5c8d239 · outbound
This paper cites GAP9 processor — GreenWaves Technologies,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning GAP9 processor — GreenWaves Technologies,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.992568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d822244b-3949-4aba-87bf-d37ab9e54dd4 · outbound
This paper cites Fspen: an ultra-lightweight network for real time speech enah- ncment,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Fspen: an ultra-lightweight network for real time speech enah- ncment,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.984259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9a1c96f8-4016-4f71-9bcf-ac4fc79b6ffd · outbound
This paper cites An investigation of incorporating mamba for speech enhancement,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning An investigation of incorporating mamba for speech enhancement,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.976426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5f78d606-598e-4203-8d4c-11b4bbd81577 · outbound
This paper cites Mp-senet: A speech enhance- ment model with parallel denoising of magnitude and phase spec- tra,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Mp-senet: A speech enhance- ment model with parallel denoising of magnitude and phase spec- tra,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.968468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 867ec122-94b2-43da-b423-b23fb9919ac9 · outbound
This paper cites Mlp-mixer: An all-mlp architecture for vi- sion,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Mlp-mixer: An all-mlp architecture for vi- sion,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.960408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d604d29d-6457-4a4b-97e9-2eaf1807f72a · outbound
This paper cites Hyper- conformer: Multi-head hypermixer for efficient speech recogni- tion,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Hyper- conformer: Multi-head hypermixer for efficient speech recogni- tion,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.952878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32e8fffc-d7ff-42d5-aa5a-122a8ad9b794 · outbound
This paper cites Sum- marymixing: A linear-complexity alternative to self-attention for speech recognition and understanding,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Sum- marymixing: A linear-complexity alternative to self-attention for speech recognition and understanding,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.945177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 36f89277-2bd6-42d8-a9dd-8ba0919439a7 · outbound
This paper cites Personalized speech enhancement: new models and comprehensive evaluation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Personalized speech enhancement: new models and comprehensive evaluation,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.937301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 001ebdb3-c62a-454f-a1a4-8b5cda7209e6 · outbound
This paper cites Ac- celerating rnn-based speech enhancement on a multi-core mcu with mixed fp16-int8 post-training quantization,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Ac- celerating rnn-based speech enhancement on a multi-core mcu with mixed fp16-int8 post-training quantization,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.928828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 17f848d9-e2cc-4763-a470-d44c2f83b83f · outbound
This paper cites Conv-tasnet: Surpassing ideal time–frequency magnitude masking for speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Conv-tasnet: Surpassing ideal time–frequency magnitude masking for speech separation,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.920423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 16385414-2237-4ad1-83c8-046e0a6d0868 · outbound
This paper cites Mossformer: Pushing the performance limit of monaural speech separation using gated single-head trans- former with convolution-augmented joint self-attentions,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Mossformer: Pushing the performance limit of monaural speech separation using gated single-head trans- former with convolution-augmented joint self-attentions,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.912650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8aca4145-761e-4d4e-99e6-0f87907aaab7 · outbound
This paper cites Separate and recon- struct: Asymmetric encoder-decoder for speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Separate and recon- struct: Asymmetric encoder-decoder for speech separation,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.904498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b1209c69-1f41-405a-a65d-83f175b10421 · outbound
This paper cites Spmamba: State-space model is all you need in speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Spmamba: State-space model is all you need in speech separation,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.896751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4e596eea-c258-4e33-838e-1998208f1262 · outbound
This paper cites Stft- domain neural speech enhancement with very low algorithmic la- tency,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Stft- domain neural speech enhancement with very low algorithmic la- tency,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.888983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 99f383b6-fd22-4cb8-835e-18e313e3e936 · outbound
This paper cites A perceptually-motivated approach for low- complexity, real-time enhancement of fullband speech,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning A perceptually-motivated approach for low- complexity, real-time enhancement of fullband speech,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.881216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b464c632-e3bc-4d7e-b0cc-09a2dc8f97ec · outbound
This paper cites Speakerbeam-ss: Real-time target speaker extraction with lightweight conv-tasnet and state space modeling,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Speakerbeam-ss: Real-time target speaker extraction with lightweight conv-tasnet and state space modeling,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.873365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 14f732d9-104b-479d-b384-1dd7160cea57 · outbound
This paper cites Deepfilternet2: Towards real-time speech enhancement on em- bedded devices for full-band audio,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Deepfilternet2: Towards real-time speech enhancement on em- bedded devices for full-band audio,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.864888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1e5b07cb-9b5b-422a-b231-cd97ebaa4f78 · outbound
This paper cites Real-time target sound extraction,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Real-time target sound extraction,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.857038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 40f878ca-ac55-4282-be7c-38ce01a3ebe3 · outbound
This paper cites The 2nd clar- ity enhancement challenge for hearing aid speech intelligibility enhancement: Overview and outcomes,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning The 2nd clar- ity enhancement challenge for hearing aid speech intelligibility enhancement: Overview and outcomes,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.849106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 88e7a0f5-57c3-457a-b90a-1745b0455105 · outbound
This paper cites Hello edge: Keyword spotting on microcontrollers,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Hello edge: Keyword spotting on microcontrollers,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.840573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d56a21dd-2517-4b76-a686-bc782233326c · outbound
This paper cites Tinysv: Speaker verification in tinyml with on-device learning,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Tinysv: Speaker verification in tinyml with on-device learning,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.832476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ebc0c23-5d2d-4786-bfe8-2ea6523dca76 · outbound
This paper cites “it os okay to be uncommon.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning “it os okay to be uncommon
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.824508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 201d6d4a-3740-4b22-b963-559450d36f5f · outbound
This paper cites Tinylstms: Efficient neural speech enhancement for hearing aids,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Tinylstms: Efficient neural speech enhancement for hearing aids,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.816701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6fbbc7d4-e164-4bda-9b48-2ffd7076ae28 · outbound
This paper cites Towards fully quantized neural networks for speech enhancement,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Towards fully quantized neural networks for speech enhancement,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.808457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3cd40dd3-116a-42a8-ab3f-6f29c3e6f6be · outbound
This paper cites Real- time denoising and dereverberation wtih tiny recurrent u-net,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Real- time denoising and dereverberation wtih tiny recurrent u-net,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.800546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f2f09927-5c97-48b2-a820-b72daa879753 · outbound
This paper cites Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in hearing aids,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in hearing aids,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.792657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 486d54ca-a103-4a6f-9126-1476afe29854 · outbound
This paper cites Two-step knowl- edge distillation for tiny speech enhancement,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Two-step knowl- edge distillation for tiny speech enhancement,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.784536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 40aa389a-48ea-423b-9178-74fa2b9210f2 · outbound
This paper cites Distilled binary neural network for monaural speech separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Distilled binary neural network for monaural speech separation,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.776393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ea4c052c-74a3-4714-b472-618aa0167b16 · outbound
This paper cites Fully quantized neural networks for audio source separation,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Fully quantized neural networks for audio source separation,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.767877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ee30f922-d987-46ad-996a-a89810da7838 · outbound
This paper cites A survey of quantization methods for efficient neural network inference,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning A survey of quantization methods for efficient neural network inference,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.759706Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 13865733-979b-4871-8b95-e0b5e580fd2b · outbound
This paper cites Lib- rispeech: An asr corpus based on public domain audio books,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Lib- rispeech: An asr corpus based on public domain audio books,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.750895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 851a7785-db7c-4d0b-bce5-cacaf020e74d · outbound
This paper cites Cstr vctk corpus: English multi-speaker corpus for speech synthesis,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Cstr vctk corpus: English multi-speaker corpus for speech synthesis,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.741160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 92612679-133e-40c3-9c2f-c4454a943bce · outbound
This paper cites Speaker diarization with lstm,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Speaker diarization with lstm,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.730919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 759487d3-21fc-49db-a440-081eb74bd8ef · outbound
This paper cites Dnsmos p.835: A non- intrusive perceptual objective speech quality metric to evaluate noise suppressors,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Dnsmos p.835: A non- intrusive perceptual objective speech quality metric to evaluate noise suppressors,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.536404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9d47cbd3-c491-4d05-97ab-a44261acc264 · outbound
This paper cites Wireless hearables with programmable speech ai accelerators,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Wireless hearables with programmable speech ai accelerators,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.338627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e6acaf54-878e-4839-9e7b-253c1071a592 · outbound
This paper cites Hybrid neural networks for on-device directional hearing,.
A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning Hybrid neural networks for on-device directional hearing,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T04:44:50.157601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.