Real-time Speech Emotion Recognition by ANN
📜 Abstract
The objective of this thesis is to design an Artificial Neural Network (ANN) solution for real-time speech emotion recognition. Human-computer interaction research has shown an increased interest in understanding and interpreting human emotions from speech signals. The power of speech emotion recognition can significantly enhance user experiences in various interactive applications. This study investigates the methodologies for processing and analyzing emotional speech data by deploying ANN architectures. Python programming language and its libraries are employed for implementing the proposed models. Experimental results are conducted to assess the performance of these models and understand their capabilities in recognizing emotions from speech in real-time scenarios.
✨ Summary
This paper, authored by Berk Kapicioglu in November 2013, focuses on designing a real-time speech emotion recognition system using Artificial Neural Networks (ANNs). It explores methods for processing and analyzing emotional speech data while applying ANN architectures optimized for real-time scenarios. The study uses Python for implementation and conducts experiments to evaluate model performance.
Despite its intriguing premise, the paper does not appear to be widely cited in subsequent research. A web search did not return significant scholarly articles or industry applications that build directly on this thesis. As this work primarily resides as a master’s thesis, its influence on further research may have been modest, and it may not have significantly impacted speech emotion recognition advancements documented in mainstream academic publications or industry practices as of now.