From the voice-to-text feature on your phone to the captions that make videos more accessible, speech transcription is already woven into everyday life. Behind the scenes, artificial intelligence is ...

For the next five months, machine learning gurus can try to best predict the speech of a brain-computer interface (BCI) user who lost the ability to speak due to a neurodegenerative disease ...

In this study, we propose a high-precision, fine-grained emotion recognition system based on multisource throat physiological signals. The system collects signals through optimized flexible ...

In a defining moment for Arabic-language artificial intelligence, CNTXT AI has unveiled Munsit, a next-generation Arabic speech recognition model that is not only the most accurate ever created for ...

This project detects human emotions from speech using MFCC feature extraction and machine learning models including SVM, Random Forest, CNN, and XGBoost. By analyzing voice patterns, the system ...

As powerful as today’s Automatic Speech Recognition (ASR) systems are, the field is far from “solved.” Researchers and practitioners are grappling with a host of challenges that push the boundaries of ...

A Speech Emotion Recognition project using CREMA, RAVDESS, SAVEE, and TESS datasets. Implements machine learning techniques to classify emotions from speech.