AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The obtained experimental results prove the superiority of the proposed hybrid representation over the single Wavelet and MFCC features, achieving the overall recognition accuracy of 99% and average accuracy of 94%.
Speech Emotion Recognition (SER) has become a key aspect in human-computer interaction, and affective computing, yet, current methods are faced with the challenge of modeling long-range context and fine-grain emotional expressions in speech signals. This paper has countered these shortcomings, giving a Transformer-base...
This work introduces ExpressNet, an optimum Multi-Layer Perceptron (MLP)-based SER model aimed to solve issues by leveraging a wide range of prosodic and spectral qualities incorporating Mel-Frequency Cepstral Coefficients (MFCCs), spectral contrast, and pitch variations.
Speech emotion recognition (SER) is used in many domains, such as translation, intelligent assistants, healthcare monitoring, large language models, and human-computer interaction. Emotion recognition in Malayalam, however, remains challenging because of the language's rich morphological structure. This work introduces...
Speech emotion recognition is an upcoming subfield of automatic speech recognition that shares multiple similarities with mood recognition in music signals. Audio signals containing human speech are used as input to classification algorithms trained to recognize emotions in the form of audio features. This thesis outline...
OBJECTIVE Speech Emotion Recognition (SER) has gained significant research attention over the past three decades owing to its diverse real-world applications, including human-computer interaction, healthcare, call centers, automotive systems, education, and security. The primary goal of SER is to accurately identify hu...
Electroencephalogram (EEG) signals used for emotion classification have gained a lot of research interest. However, improving the efficacy of emotion recognition across individuals is difficult. Due to the weak generalizability of characteristics across individuals, it has always been challenging to identify cross-subj...
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