First published 9/28/2026, 7:02:15 AM · Last updated 9/28/2026, 7:02:30 AM

Adaptive multi-feature speech emotion recognition using improved whale optimization with Lévy-flight exploration and elite opposition learning

A journal article published by Elsevier BV on 1 December 2026 describes an adaptive multi-feature speech emotion recognition method that applies an improved whale optimization algorithm with Lévy-flight exploration and elite opposition learning. The supplied source data include only the title, publisher, and publication date and no additional methodological or result details.

Categories: technology, science-and-space

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 6/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 3/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The paper reports a new algorithmic approach relevant to research and applications in speech emotion recognition.

Sources

Adaptive multi-feature speech emotion recognition using improved whale optimization with Lévy-flight exploration and elite opposition learning

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