English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the era of advanced artificial intelligence and human-computerinteraction, identifying emotions in spoken language is paramount. Thisresearch explores the integration of deep learning techniques in speech emotionrecognition, offering a comprehensive solution to the challenges associatedwith speaker diarization and emotion identification. It introduces a frameworkthat combines a pre-existing speaker diarization pipeline and an emotionidentification model built on a Convolutional Neural Network (CNN) to achievehigher precision. The proposed model was trained on data from five speechemotion datasets, namely, RAVDESS, CREMA-D, SAVEE, TESS, and Movie Clips, outof which the latter is a speech emotion dataset created specifically for thisresearch. The features extracted from each sample include Mel FrequencyCepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Root Mean Square (RMS),and various data augmentation algorithms like pitch, noise, stretch, and shift.This feature extraction approach aims to enhance prediction accuracy whilereducing computational complexity. The proposed model yields an unweightedaccuracy of 63%, demonstrating remarkable efficiency in accurately identifyingemotional states within speech signals.

EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks