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Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders

Research article (Electronics, 2022) · cited 30× · AI/ML
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Summary

Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders is a scholarly article[1].

Key Facts

  • Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders's instance of is recorded as scholarly article[2].

Related Entities

Mohamed S. Abdallah

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders. Retrieved May 24, 2026, from https://4ort.xyz/entity/modeling-speech-emotion-recognition-via-attention-oriented-parallel-cnn-encoders
MLA “Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/modeling-speech-emotion-recognition-via-attention-oriented-parallel-cnn-encoders.
BibTeX @misc{4ortxyz_modeling-speech-emotion-recognition-via-attention-oriented-parallel-cnn-encoders_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders}}, year = {2026}, url = {https://4ort.xyz/entity/modeling-speech-emotion-recognition-via-attention-oriented-parallel-cnn-encoders}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders — https://4ort.xyz/entity/modeling-speech-emotion-recognition-via-attention-oriented-parallel-cnn-encoders (retrieved 2026-05-24)

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